1.7: Specialized Infusions
In addition to the provision of IV fluid therapy and medication administration via the intravenous route, nurses must also be aware of specialized infusions and the safety implications that may be associated with these types of infusions. Two types of specialized infusions include medication administered via a patient-controlled analgesia (PCA) pump and those administered via epidural infusion. There are many unique advantages and risks associated with specialized infusions, and nurses must be aware of the safety implications and associated interventions to ensure quality patient care.
Patient-Controlled Analgesia
Patient-controlled analgesia (PCA) is a method of pain management that allows hospitalized patients with severe pain to safely self-administer opioid medications using a programmed pump according to their level of discomfort. A computerized pump contains a syringe of pain medication and is connected directly to a patient’s intravenous (IV) line. Doses of medication can be administered as either a basal (i.e., continuous) rate, self-administered as needed on demand, or a combination of both methods. Doses are self-administered by the patient by pressing a button that initiates a “demand” for medication. However, the pump is programmed to only allow administration of medication every set number of minutes with a maximum dose of medication every hour. These pump settings and the design of the system require the patient to be alert enough to press the button to receive a demand dose. These design features are safety measures to help prevent overmedication that can cause sedation and respiratory depression. However, despite these safety measures it is vital for nurses to closely monitor patients receiving PCA for respiratory depression. [1] See Figure 1.18 [2] for an image of a PCA pump.
Patient-controlled analgesia can be very beneficial for select patients, reducing the time for medication administration and allowing pain medication to reach the patient more rapidly to reduce pain symptoms. This can help prevent out-of-control pain levels that require a significant length of time to resolve. The principles behind the method of patient-controlled analgesia involve the patient’s experience of pain, subsequent administration of a drug via the PCA pump, the drug action within the bloodstream, and the patient response. Depending on the level of pain relief, the patient may experience symptom resolution or may proceed with a dose demand process by activating the PCA pump at a subsequent interval. The patient becomes an active party in medicating themselves to help control the pain experience.
It is critical that patients who are prescribed a PCA pump with demand dosing understand the safety principles behind the medication administration. Patients and their family members must be aware that only the patient is allowed to initiate the demand dose by pressing the medication button to ensure they are alert enough to do so. In this manner, oversedation and life-threatening respiratory depression can be avoided with demand dosing.
Additionally, patients should receive education regarding the frequency with which they will receive a demand dose in order to be reassured that they cannot accidentally overdose themselves by pressing the demand button too frequently. Cues such as an audible “beep” often accompany the demand dose and can help cue the patient that the medication was administered. Conversely, if the demand button has been initiated too closely within the programmed interval, an audible “chirp” may signal to the patient that the demand dose was too soon. The patient should receive instruction that if the chirp has been noted, they may wish to wait a few minutes and reinitiate the demand.
Patients with impaired cognition or restricted upper extremity dexterity may not be appropriate for PCA. If a nurse has concerns regarding the appropriateness of self-administration of pain medication via a PCA pump based on characteristics and clinical condition of an individual patient, the prescribing provider should be notified, and alternate pain control measures should be considered.
The basic principles of a PCA medication order include the following:
Loading dose : Ordered amount of medication administered at the time of PCA initiation
Demand dose : Medication dose given on activation of demand (pressing the demand button)
Lockout interval : Time period in which no follow-up demand dose may be administered (even if demand button is activated)
Basal infusion : Continuous rate of medication administration, regardless of demand attempts
Lockout maximum : The maximum dose of medication that can be administered within a certain period, commonly prescribed to 1 hour limit
Breakthrough bolus dose : A dose of opioid or non-opioid medication administered by the nurse for breakthrough pain.
Medications that are commonly prescribed for PCA administration include opioids such as morphine, hydromorphone, and fentanyl. Nurses must be familiar with specific institutional policies and protocols regarding the use of PCA administration. These protocols commonly include the use of specific provider orders, safety checks, and patient monitoring. Many organizations have transitioned to the use of preprinted PCA order sets to help guarantee that required PCA dosing parameters are appropriately addressed. Within a PCA order, the prescriber must include the following information: identification of the specific medication to be used in the PCA pump, the medication concentration, the specific quantity of the demand dose, the lockout interval, and the order lockout maximum. When a nurse is initiating a PCA pump, it is critical that the order be reviewed and verified with another registered nurse according to agency policy. The double-check verification helps ensure that the pump is programmed correctly, and lockout limits are included to decrease the risk of accidentally self-administering too much medication. [3]
Due to the potential adverse effects of opioids administered via a PCA pump, there is a significant risk for patient safety requiring specific monitoring by the nurse. Continuous pulse oximetry, end tidal carbon dioxide monitoring, and frequent vital signs monitoring may be required with initiation and ongoing use of a PCA pump. Individuals with obstructive airway disease, sleep apnea, obesity, renal or hepatic impairment, and recent analgesia, sedation, or anesthesia are at increased risk of complications associated with PCA administration, such as decreased level of consciousness, respiratory distress, and hypotension. [4] Dosing parameters must account for patient specific factors and individualized clinical need when initiated by the prescribing provider. Additionally, nurses must be especially vigilant when first initiating PCA infusion because initial infusion is when administration complications may be more frequently observed.
Please review the “ Adverse Effects of Opioids ” section in Open RN Nursing Fundamentals for more information about potential patient side effects.
Nursing staff must also be cognizant of the post-administration assessment and individualized response of each patient to PCA. Even though the patient is actively participating in their pain control plan through self-administration, pain response must be thoroughly documented. Nurses must frequently assess the patient’s number of demands and subsequent doses received. Nurses should also carefully monitor the patient’s vital signs, pain self-report, and nonverbal indications of the pain experience. This focused pain assessment helps ensure that the pain level is adequately controlled.
The use of PCA pumps require vigilant checks by two licensed registered nurses minimally at the start of every shift. The secure PCA pump holds the prescribed opioid medication in a locked device with a demand button that is given to the patient. Keys to the PCA pump are safely stored in secure areas such as within an automated drug dispensing machine. The pumps also have an access code to ensure that tampering with the pump settings does not occur. To document the amount and frequency of pain medication the patient is receiving, as well as to prevent drug diversion, the settings on the pump are checked at the end of every shift as part of the bedside report. The incoming and outgoing nurses double-check and document the pump settings, the amount of medication administered during the previous shift, and the amount of medication left in the syringe. When a PCA is discontinued, nurses must carefully document the remainder of medication left in the PCA syringe and follow organizational policies related to narcotic wasting and documentation.
PCA pumps often have specialized tubing and connections to prevent inadvertent connection of secondary medication to the dedicated PCA line above the pump. If a bolus of medication is administered via a dedicated PCA line below the pump, the nurse must ensure it is compatible with the PCA medication.
Epidural
Epidural medication administration is a type of specialized infusion that is different than intravenous infusion. The use of an epidural involves administering analgesics and anesthetics directly into the spinal fluid via an epidural catheter for severe pain management. It is inserted by specially trained health professionals and requires additional safety measures and nurse monitoring to help prevent patient complications. Safety measures such as specially colored tubing and different Luer lock systems are incorporated to ensure only epidural medications are administered via the epidural line.
Epidurals are typically used with surgical procedures or during labor and delivery. Epidural infusions may also be used to treat chronic pain that has not responded to more conservative treatments. Common anesthetics that are used in epidural infusions include bupivacaine and ropivacaine administered alone or in combination with opioid medications. Anesthetics and analgesics administered via the epidural route work synergistically (i.e., cooperatively) to provide greater pain relief at lower doses. [5] See Figure 1.19 [6] for an image of an epidural pump with colored tubing and an image of freshly inserted lumbar epidural catheter. The epidural site has been prepared with tincture of iodine, and the dressing has not yet been applied.
Epidural catheters are inserted and may only be repositioned by trained anesthesia professionals. The anesthesia provider will administer a test dose of medication into the epidural to ensure that it is appropriately positioned. Once adequate positioning has been confirmed, the anesthesia provider will record the documented block height by assessing the patient’s response to cold at various levels of the thoracic spine. This should be recorded in the patient’s chart and is the subsequent block height for all future neurosensory assessments. Any block level that progresses up the body or is recorded above a level of T4 (fourth thoracic vertebra) must be immediately communicated to the anesthesia provider to prevent respiratory distress and cardiovascular collapse. [7]
Once the catheter has been inserted and the position safely confirmed, a prescribing provider will order the medication for epidural infusion. The order must contain the medication name, concentration, and infusion rate. The epidural pump is similar to a PCA pump in that it must be locked and the infusion settings securely programmed. Some epidural pumps allow patients to self-administer on-demand doses. The initiation of the epidural medication may involve setting the pump to infuse at a constant rate or by providing an initial bolus dose and then a constant infusion rate.
Nurses caring for patients receiving epidural medications must monitor patient response to the medication and the epidural insertion site for potential complications associated with epidural infusion. Follow agency policy regarding focused assessments that typically include the patient’s pain management response, vital signs, and motor/sensory checks. Signs of respiratory depression must be carefully monitored and reported to the provider if the patient exhibits a respiratory rate of less than eight breaths/minute, declining oxygen saturation, or decreased level of consciousness. Nurses must also carefully observe the patient’s blood pressure and heart rate with epidural analgesia. Changes in vital signs may be associated with the epidural infusion but can also be the result of post-procedural complications, so vigilant postoperative monitoring must occur. Patients receiving epidural administration are also at risk for bradycardia, so atropine should be readily available in case the patient experiences symptomatic bradycardia. [8] , [9]
Nurses should be prepared to assess for signs of potential clinical problems associated with epidural usage. Table 1.7 summarizes common clinical problems associated with epidural infusions.
| Clinical Problem | Potential Interventions |
|---|---|
| Inadequate pain control; partial block | Work with the anesthesia provider to adjust the patient’s position or increase infusion rate. If the block is one-sided, position the patient painful side down to allow gravity to assist the medication to travel down the nerves on the side of pain. |
| Hypotension | Hypotension may occur with epidural use due to the dilation of blood vessels. Notify the provider. The epidural rate may need to be decreased or bolus fluids administered. The patient may require placement in Trendelenberg position. |
| Bradycardia | May occur as the result of blockage of sympathetic nerves. If symptomatic, notify the provider and anticipate possible administration of atropine. |
| Increasing sensation block height/difficulty breathing | The patient may first report signs of tingling in fingers, especially the fifth digits. Notify the provider, turn off infusion, elevate the patient’s head, and administer oxygen. |
| Motor blockage | Leg weakness may occur as a result of an epidural block to the low motor nerves. A reduction in rate may be needed if significant paralysis is experienced. Be cautious of mobility implications associated with reduced sensation. |
| Respiratory depression | If a patient experiences respiratory depression of less than eight breaths per minute, stop the infusion and notify the provider. If indicated, request emergency assistance and administer rescue breaths via a bag-valve-mask. |
| Catheter disconnection | Disconnection of the epidural line presents an increased infection risk. Immediately contact the anesthesia provider, but do NOT reconnect the line. The end of the line may be covered with sterile gauze, according to agency policy, until the anesthesia provider arrives. |
| Catheter migration | Migration of the epidural catheter into a blood vessel may result in exacerbated respiratory depression and sedation. Monitor the patient for any report of tingling around the mouth, numbness, twitching, convulsion, or cardiac arrest. Immediately stop the epidural infusion and provide emergency assistance and cardiopulmonary support. |
Common side effects of epidural medication infusion include nausea and itching associated with the specific medications included in the infusions. Many times, these side effects can be adequately managed with medications, and the epidural infusion may continue. Another common side effect is urinary retention. Nurses should monitor urine output for patients who do not have a urinary catheter in place. Intermittent or indwelling catheterization may be required to drain the bladder and prevent discomfort. [12] , [13]
Review a handout on how to assess sensation and motor function for a patient receiving epidural anesthesia.- Soffin, E. M., & Liu, S. S. (2018). Patient-controlled analgesia. In H. T. Benzon, S. N. Raja, S. M. Fishman, S. S., & S. P. Cohen’s (Eds.). Essentials of pain medicine (4th ed., pp. 117-122.e2). Elsevier. https://doi.org/10.1016/B978-0-323-40196-8.00013-9 ↵
- "alaris-pca-module-IF-0518-0034" by unknown author used on the basis of Fair Use. Access original image at https://www.bd.com/en-us/products-and-solutions/products/product-families/bd-alaris-pca-module . ↵
- Soffin, E. M., & Liu, S. S. (2018). Patient-controlled analgesia. In H. T. Benzon, S. N. Raja, S. M. Fishman, S. S., & S. P. Cohen’s (Eds.). Essentials of pain medicine (4th ed., pp. 117-122.e2). Elsevier. https://doi.org/10.1016/B978-0-323-40196-8.00013-9 ↵
- Soffin, E. M., & Liu, S. S. (2018). Patient-controlled analgesia. In H. T. Benzon, S. N. Raja, S. M. Fishman, S. S., & S. P. Cohen’s (Eds.). Essentials of pain medicine (4th ed., pp. 117-122.e2). Elsevier. https://doi.org/10.1016/B978-0-323-40196-8.00013-9 ↵
- Benzon, H. T., Raja, S., Liu, S. S., Fishman, S., Cohen, S. P., & Hurley, R. W. (Eds.). (2018). Essentials of pain medicine (4th ed., pp. 117-122.e2). Elsevier. https://doi.org/10.1016/C2014-0-03837-3 ↵
- “ PCA-01.JPG ” by DiverDave is licensed under CC BY-SA 3.0 and Epidural.JPG” by User:Ravedave is licensed under CC BY-SA 3.0 ↵
- Galligan, M. (2020). Care and management of patients receiving epidural analgesia. Nursing Standard, 35 (12), 77-82. https://doi.org/10.7748/ns.2020.e11573 ↵
- Galligan, M. (2020). Care and management of patients receiving epidural analgesia. Nursing Standard, 35 (12), 77-82. https://doi.org/10.7748/ns.2020.e11573 ↵
- Woodall, W. G. (2019). Care for the patient receiving epidural analgesia. Medsurg Nursing, 28 (3), 194-195. https://go.openathens.net/redirector/liberty.edu?url=https://www.proquest.com/scholarly-journals/care-patient-receiving-epidural-analgesia/docview/2242627838/se-2?accountid=12085 ↵
- Galligan, M. (2020). Care and management of patients receiving epidural analgesia. Nursing Standard, 35 (12), 77-82. https://doi.org/10.7748/ns.2020.e11573 ↵
- Woodall, W. G. (2019). Care for the patient receiving epidural analgesia. Medsurg Nursing, 28 (3), 194-195. https://go.openathens.net/redirector/liberty.edu?url=https://www.proquest.com/scholarly-journals/care-patient-receiving-epidural-analgesia/docview/2242627838/se-2?accountid=12085 ↵
- Galligan, M. (2020). Care and management of patients receiving epidural analgesia. Nursing Standard, 35 (12), 77-82. https://doi.org/10.7748/ns.2020.e11573 ↵
- Woodall, W. G. (2019). Care for the patient receiving epidural analgesia. Medsurg Nursing, 28 (3), 194-195. https://go.openathens.net/redirector/liberty.edu?url=https://www.proquest.com/scholarly-journals/care-patient-receiving-epidural-analgesia/docview/2242627838/se-2?accountid=12085 ↵


3.8: Reading- Firmware
Introduction
In electronic systems and computing, firmware is a tangible electronic component with embedded software instructions, such as a BIOS. Typically, those software instructions are used to tell an electronic device how to operate. As of 2013, most firmware can be updated. Typical examples of devices containing firmware are embedded systems (such as traffic lights, consumer appliances, and digital watches), computers, computer peripherals, mobile phones, and digital cameras. The firmware contained in these devices provides the control program for the device.
Firmware is held in non-volatile memory devices such as ROM, EPROM, or flash memory. Changing the firmware of a device may rarely or never be done during its economic lifetime; some firmware memory devices are permanently installed and cannot be changed after manufacture. Common reasons for updating firmware include fixing bugs or adding features to the device. This may require ROM integrated circuits to be physically replaced, or flash memory to be reprogrammed through a special procedure. Firmware such as the ROM BIOS of a personal computer may contain only elementary basic functions of a device and may only provide services to higher-level software. Firmware such as the program of an embedded system may be the only program that will run on the system and provide all of its functions.
Before integrated circuits, other firmware devices included a discrete semiconductor diode matrix. The Apollo guidance computer had firmware consisting of a specially manufactured core memory plane, called “core rope memory,” where data were stored by physically threading wires through (1) or around (0) the core storing each data bit.
Origin of the term
Ascher Opler coined the term “firmware” in a 1967 Datamation article. Originally, it meant the contents of a writable control store (a small specialized high speed memory), containing microcode that defined and implemented the computer’s instruction set, and that could be reloaded to specialize or modify the instructions that the central processing unit (CPU) could execute. As originally used, firmware contrasted with hardware (the CPU itself) and software (normal instructions executing on a CPU). It was not composed of CPU machine instructions, but of lower-level microcode involved in the implementation of machine instructions. It existed on the boundary between hardware and software; thus the name “firmware”.
Still later, popular usage extended the word “firmware” to denote anything ROM-resident, including processor machine-instructions for BIOS, bootstrap loaders, or specialized applications.
Until the mid-1990s, updating firmware typically involved replacing a storage medium containing firmware, usually a socketed ROM integrated circuit. Flash memory allows firmware to be updated without physically removing an integrated circuit from the system. An error during the update process may make the device non-functional, or “bricked”.
Personal computers
In some respects, the various firmware components are as important as the operating system in a working computer. However, unlike most modern operating systems, firmware rarely has a well-evolved automatic mechanism of updating itself to fix any functionality issues detected after shipping the unit.
The BIOS may be “manually” updated by a user, using a small utility program. In contrast, firmware in storage devices (hard disks, DVD drives, flash storage) rarely gets updated, even when flash (rather than ROM) storage is used for the firmware; there are no standardized mechanisms for detecting or updating firmware versions.
Most computer peripherals are themselves special-purpose computers. Devices such as printers, scanners, cameras and USB flash drives have internally stored firmware; some devices may also permit field upgrading of their firmware.
Some low-cost peripherals no longer contain non-volatile memory for firmware, and instead rely on the host system to transfer the device control program from a disk file or CD.
Consumer products
As of 2010 most portable music players support firmware upgrades. Some companies use firmware updates to add new playable file formats (codecs); iriver added Vorbis playback support this way, for instance. Other features that may change with firmware updates include the GUI or even the battery life. Most mobile phones have a Firmware Over The Air firmware upgrade capability for much the same reasons; some may even be upgraded to enhance reception or sound quality, illustrating the fact that firmware is used at more than one level in complex products (in a CPU-like microcontroller versus in a digital signal processor, in this particular case).
Automobiles
Since 1996 most automobiles have employed an on-board computer and various sensors to detect mechanical problems. As of 2010 modern vehicles also employ computer-controlled ABS systems and computer-operated Transmission Control Units (TCU). The driver can also get in-dash information while driving in this manner, such as real-time fuel-economy and tire-pressure readings. Local dealers can update most vehicle firmware.
Examples
Examples of firmware include:
-
In consumer products:
- Timing and control systems for washing machines
- Controlling sound and video attributes, as well as the channel list, in modern TVs
- EPROM chips used in the Eventide H-3000 series of digital music processors
-
In computers:
- The BIOS found in IBM-compatible personal computers
- The (U)EFI -compliant firmware used on Itanium systems, Intel-based computers from Apple , and many Intel desktop computer motherboards
- Open Firmware , used in SPARC -based computers from Sun Microsystems and Oracle Corporation , PowerPC -based computers from Apple, and computers from Genesi
- ARCS , used in computers from Silicon Graphics
- Kickstart , used in the Amiga line of computers ( POST , hardware init + Plug and Play auto-configuration of peripherals, kernel , etc.)
- RTAS (Run-Time Abstraction Services), used in computers from IBM
- The Common Firmware Environment (CFE)
-
In
routers
and
firewalls
:
- LibreWRT – a 100% free software router distribution based on the Linux-libre kernel
- IPFire – an open-source firewall/router distribution based on the Linux kernel
- fli4l – an open-source firewall/router distribution based on the Linux kernel
- OpenWrt – an open-source firewall/router distribution based on the Linux kernel
- m0n0wall – an embedded firewall distribution of FreeBSD
- In NAS systems:
Flashing
Flashing involves the overwriting of existing firmware or data on EEPROM modules present in an electronic device with new data. This can be done to upgrade a device or to change the provider of a service associated with the function of the device, such as changing from one mobile phone service provider to another or installing a new operating system. If firmware is upgradable, it is often done via a program from the provider, and will often allow the old firmware to be saved before upgrading so it can be reverted to if the process fails, or if the newer version performs worse.
Firmware hacking
Sometimes, third parties create an unofficial new or modified (“aftermarket”) version of firmware to provide new features or to unlock hidden functionality; this is referred to as custom firmware (also “Custom Firmware” in the video game console community). An example is Rockbox as a firmware replacement forportable media players. There are many homebrew projects for video game consoles, which often unlock general-purpose computing functionality in previously limited devices (e.g., running Doom on iPods).
Firmware hacks usually take advantage of the firmware update facility on many devices to install or run themselves. Some, however, must resort to exploits in order to run, because the manufacturer has attempted to lock the hardware to stop it from running unlicensed code.
Most firmware hacks are free software.
HDD firmware hacks
The Moscow-based Kaspersky Lab discovered that a group of developers it refers to as the “Equation Group” has developed hard disk drive firmware modifications for various drive models, containing a trojan horse that allows data to be stored on the drive in locations that will not be erased even if the drive is formatted or wiped. Although the Kaspersky Lab report did not explicitly claim that this group is part of the United States National Security Agency (NSA), evidence obtained from the code of various Equation Group software suggests that they are part of the NSA.
Researchers from the Kaspersky Lab categorized the undertakings by Equation Group as the most advanced hacking operation ever uncovered, also documenting around 500 infections caused by the Equation Group in at least 42 countries.
Security risks
Mark Shuttleworth, founder of the Ubuntu Linux distribution, has described proprietary firmware as a security risk, saying that “firmware on your device is theNSA’s best friend” and calling firmware “a trojan horse of monumental proportions”. He has pointed out that low-quality, nonfree firmware is a major threat to system security: “Your biggest mistake is to assume that the NSA is the only institution abusing this position of trust – in fact, it’s reasonable to assume that all firmware is a cesspool of insecurity, courtesy of incompetence of the highest degree from manufacturers, and competence of the highest degree from a very wide range of such agencies”. As a solution to this problem, he has called for declarative firmware, which would describe “hardware linkage and dependencies” and “should not include executable code”.
Custom firmware hacks have also focused on injecting malware into devices such as smartphones or USB devices. One such smartphone injection was demonstrated on the Symbian OS at MalCon, a hacker convention. A USB device firmware hack called BadUSB was presented at Black Hat USA 2014conference, demonstrating how a USB flash drive microcontroller can be reprogrammed to spoof various other device types in order to take control of a computer, exfiltrate data, or spy on the user. Other security researchers have worked further on how to exploit the principles behind BadUSB, releasing at the same time the source code of hacking tools that can be used to modify the behavior of different USB devices.
- Firmware. Provided by : Wikipedia. Located at : https://en.wikipedia.org/wiki/Firmware . License : CC BY-SA: Attribution-ShareAlike
- firmware circuit. Located at : https://pixabay.com/en/lpc-ic-testing-firmware-circuit-301425/ . License : CC0: No Rights Reserved
- Tesla Series S - Computer Panel. Authored by : Wesley Fryer. Located at : https://www.flickr.com/photos/wfryer/8081558847/ . License : CC BY: Attribution


4.2: Annotating
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Once the data have been extracted from the corpus (and, if necessary, false positives have been removed), they typically have to be annotated in terms of the variables relevant for the research question. In some cases, the variables and their values will be provided externally; they may, for example, follow from the structure of the corpus itself, as in the case of BRITISH ENGLISH vs. AMERICAN ENGLISH defined as “occurring in the LOB corpus” and “occurring in the BROWN corpus” respectively. In other cases, the variables and their values may have been operationalized in terms of criteria that can be applied objectively (as in the case of Length defined as “number of letters”). In most cases, however, some degree of interpretation will be involved (as in the case of Animacy or the metaphors discussed above). Whatever the case, we need an annotation scheme – an explicit statement of the operational definitions applied. Of course, such an annotation scheme is especially important in cases where interpretative judgments are involved in categorizing the data. In this case, the annotation scheme should contain not just operational definitions, but also explicit guidelines as to how these definitions should be applied to the corpus data. These guidelines must be explicit enough to ensure a high degree of agreement if different annotators (sometimes also referred to as coders or raters apply it to the same data. Let us look at each of these aspects in some detail.
4.2.1 Annotating as interpretation
First of all, it is necessary to understand that the categorization of corpus data is an interpretative process in the first place. This is true regardless of the kind of category.
Even externally given categories typically receive a specific interpretation in the context of a specific research project. In the simplest case, this consists in accepting the operational definitions used by the makers of a particular corpus (as well as the interpretative judgments made in applying them). Take the example of BRITISH ENGLISH and AMERICAN ENGLISH used in Chapters 3 and 2: If we accept the idea that the LOB corpus contains “British English” we are accepting an interpretation of language varieties that is based on geographical criteria: British English means “the English spoken by people who live (perhaps also: who were born and grew up) in the British Isles”.
Or take the example of Sex, one of the demographic speaker variables included in many modern corpora: By accepting the values of this variable, that the corpus provides (typically MALE and FEMALE), we are accepting a specific interpretation of what it means to be “male” or “female”. In some corpora, this may be the interpretation of the speakers themselves (i.e., the corpus creators may have asked the speakers to specify their sex), in other cases this may be the interpretation of the corpus creators (based, for example, on the first names of the speakers or on the assessment of whoever collected the data). For many speakers in a corpus, these different interpretations will presumably match, so that we can accept whatever interpretation was used as an approximation of our own operation definition of Sex. But in research projects that are based on a specific understanding of Sex (for example, as a purely biological, a purely social or a purely psychological category), simply accepting the (often unstated) operational definition used by the corpus creators may distort our results substantially. The same is true of other demographic variables, like education, income, social class, etc., which are often defined on a “common sense” basis that does not hold up to the current state of sociological research.
Interpretation also plays a role in the case of seemingly objective criteria. Even though a criterion such as “number of letters” is largely self-explanatory, there are cases requiring interpretative judgments that may vary across researchers. In the absence of clear instructions they may not know, among other things, whether to treat ligatures as one or two letters, whether apostrophes or word-internal hyphens are supposed to count as letters, or how to deal with spelling variants (for example, in the BNC the noun programme also occurs in the variant program that is shorter by two letters). This kind of orthographic variation is very typical of older stages of English (before there was a somewhat standardized orthography), which causes problems not only for retrieval (cf. the discussion in Section 4.1.1 above, cf. also Barnbrook (1996), Section 8.2 for more detailed discussion), but also for a reasonable application of the criterion “number of letters”.
In such cases, the role of interpretation can be reduced by including explicit instructions for dealing with potentially unclear cases. However, we may not have thought of all potentially unclear cases before we start annotating our data, which means that we may have to amend our annotation scheme as we go along. In this case, it is important to check whether our amendments have an effect on the data we have already annotated, and to re-annotate them if necessary.
In cases of less objective criteria (such as Animacy discussed in Section <IP_ADDRESS> of Chapter 3), the role of interpretation is obvious. No matter how explicit our annotation scheme, we will come across cases that are not covered and will require individual decisions; and even the clear cases are always based on an interpretative judgment. As mentioned in Chapter 1, this is not necessarily undesirable in the same way that intuitive judgements about acceptability are undesirable; interpreting linguistic utterances is a natural activity in the context of using language. Thus, if our operational definitions of the relevant variables and values are close to the definitions speakers implicitly apply in everyday linguistic interactions, we may get a high degree of agreement even in the absence of an explicit annotation scheme, 3 and certainly, operational definitions should strive to retain some degree of linguistic naturalness in the sense of being anchored in interpretation processes that plausibly occur in language processing.
4.2.2 Annotation schemes
We can think of a linguistic annotation scheme as a comprehensive operational definition for a particular variable, with detailed instructions as to how the values of this variable should be assigned to linguistic data (in our case, corpus data, but annotation schemes are also needed to categorize experimentally elicited linguistic data). The annotation scheme would typically also include a coding scheme, specifying the labels by which these categories are to be represented. For example, the distinctions between different degrees of ANIMACY need to be defined in a way that allows us to identify them in corpus data (this is the annotation scheme, cf. below), and the scheme needs to specify names for these categories (for example, the category containing animate entities could be labelled by the codes animate, anim, #01, cat:8472, etc. – as long as we know what the label stands for, we can choose it randomly).
In order to keep different research projects in a particular area comparable, it is desirable to create annotation and coding schemes independently of a particular research project. However, the field of corpus linguistics is not well-established and methodologically mature enough yet to have yielded uncontroversial and widely applicable annotation schemes for most linguistic phenomena. There are some exceptions, such as the part-of-speech tag sets and the parsing schemes used by various wide-spread automatic taggers and parsers, which have become de facto standards by virtue of being easily applied to new data; there are also some substantial attempts to create annotation schemes for the manual annotation of phenomena like topicality (cf. Givón 1983), animacy (cf. Zaenen et al. 2004), and the grammatical description of English sentences (e.g. Sampson 1995).
Whenever it is feasible, we should use existing annotation schemes instead of creating our own – searching the literature for such schemes should be a routine step in the planning of a research project. Often, however, such a search will come up empty, or existing annotation schemes will not be suitable for the specific data we plan to use or they may be incompatible with our theoretical assumptions. In these cases, we have to create our own annotation schemes.
The first step in creating an annotation scheme for a particular variable consists in deciding on a set of values that this variable may take. As the example of ANIMACY in Chapter 3) shows, this decision is loosely constrained by our general operational definition, but the ultimate decision is up to us and must be justified within the context of our theoretical assumptions and our specific research question.
There are, in addition, several general criteria that the set of values for any variable must meet. First, they must be non-overlapping. This may seem obvious, but it is not at all unusual, for example, to find continuous dimensions split up into overlapping categories, as in the following quotation:
Hunters aged 15–25 years old participated more in non-consumptive activities than those aged 25–35 and 45–65 ( P < 0.05), as were those aged 35–45 compared to those 55–65 years old ( P < 0.05). (Ericsson & Heberlein 2002: 304).
Here, the authors obviously summarized the ages of their subjects into the following four classes: (I) 25–35, (II) 35–45, (III) 45–55 and (IV) 55–65: thus, subjects aged 35 could be assigned to class I or class II, subjects aged 45 to class II or class III, and subjects aged 55 to class III or class IV. This must be avoided, as different annotators might make different decisions, and as other researchers attempting to replicate the research will not know how we categorized such cases.
Second, the variable should be defined such that it does not conflate properties that are potentially independent of each other, as this will lead to a set of values that do not fall along a single dimension. As an example, consider the so-called Silverstein Hierarchy used to categorize nouns for (inherent) Topicality (after Deane 1987: 67):
(17) 1 st person pronoun
2 nd person pronoun
3 rd person pronoun
3 rd person demonstrative
Proper name
Kin-Term
Human and animate NP
Concrete object
Container
Location
Perceivable
Abstract
Note, first, that there is a lot of overlap in this annotation scheme. For example, a first or second person pronoun will always refer to a human or animate NP and a third person pronoun will frequently do so, as will a proper name or a kin term. Similarly, a container is a concrete object and can also be a location, and everything above the category “Perceivable” is also perceivable. This overlap can only be dealt with by an instruction of the kind that every nominal expression should be put into the topmost applicable category; in other words, we need to add an “except for expressions that also fit into one of the categories above” to every category label.
Secondly, although the Silverstein Hierarchy may superficially give the impression of providing values of a single variable that could be called TOPICALITY, it is actually a mixture of several quite different variables and their possible values. One attempt of disentangling these variables and giving them each a set of plausible values is the following:
(18) a. TYPE OF NOMINAL EXPRESSION:
PRONOUN > PROPER NAME > KINSHIP TERMS > LEXICAL NP
b. DISCOURSE ROLE:
SPEAKER > HEARER > OTHER (NEAR > FAR)
c. ANIMACY/AGENCY:
HUMAN > ANIMATE > INANIMATE
d. Concreteness:
TOUCHABLE > NON-TOUCHABLE CONCRETE > ABSTRACT
e. GESTALT STATUS:
OBJECT > CONTAINER > LOCATION
Given this set of variables, it is possible to describe all categories of the Silverstein Hierarchy as a combination of values of these variables, for example:
(19) a. 1 st Person Pronoun:
PRONOUN + SPEAKER + HUMAN + TOUCHABLE + OBJECT
b. Concrete Object:
LEXICAL NP + OTHER + INANIMATE + TOUCHABLE + OBJECT
The set of variables in (18) also allows us to differentiate between expressions that the Silverstein Hierarchy lumps together, for example, a 3 rd person pronoun could be categorized as (20a), (20b), (20c) or (20d), depending on whether it referred to a mouse , a rock , air or democracy :
(20) a. PRONOUN + OTHER + ANIMATE + TOUCHABLE + OBJECT
b. PRONOUN + OTHER + INANIMATE + TOUCHABLE + OBJECT
c. PRONOUN + OTHER + INANIMATE + NON-TOUCHABLE + OBJECT(or perhaps location, cf. in the air )
d. PRONOUN + OTHER + INANIMATE + ABSTRACT + OBJECT(or perhaps LOCATION, cf. the locative preposition in the phrase in a democracy )
There are two advantages of this more complex annotation scheme. First, it allows a more principled categorization of individual expressions: the variables and their values are easier to define and there are fewer unclear cases. Second, it would allow us to determine empirically which of the variables are actually relevant in the context of a given research question, as irrelevant variables will not show a skew in their distribution across different conditions. Originally, the Silverstein Hierarchy was meant to allow for a principled description of split ergative systems; it is possible, that the specific conflation of variables is suitable for this task. However, it is an open question whether the same conflation of variables is also suitable for the analysis of other phenomena. If we were to apply it as is, we would not be able to tell whether this is the case. Thus, we should always define our variables in terms of a single dimension and deal with complex concepts (like TOPICALITY) by analyzing the data in terms of a set of such variables.
After defining a variable (or set of variables) and deciding on the type and number of values, the second step in creating an annotation scheme consists in defining what belongs into each category. Where necessary, this should be done in the form of a decision procedure.
For example, the annotation scheme for ANIMACY mentioned in Chapter 3 (Garretson 2004; Zaenen et al. 2004) has the categories HUMAN and ORGANIZATION (among others). The category human is relatively self-explanatory, as we tend to have a good intuition about what constitutes a human. Nevertheless, the annotation scheme spells out that it does not matter by what linguistic means humans are referred to (e.g., proper names, common nouns including kinship terms, and pronouns) and that dead, fictional or potential future humans are included as well as “humanoid entities like gods, elves, ghosts, and androids”.
The category ORGANIZATION is much more complex to apply consistently, since there is no intuitively accessible and generally accepted understanding of what constitutes an organization. In particular, it needs to be specified what distinguishes an ORGANIZATION from other groups of human beings (that are to be categorized as HUMAN according to the annotation scheme). The annotation scheme defines an ORGANIZATION as a referent involving “more than one human” with “some degree of group identity”. It then provides the following hierarchy of properties that a group of humans may have (where each property implies the presence of all properties below its position in the hierarchy):
(21) +/− chartered/official
+/− temporally stable
+/− collective voice/purpose
+/− collective action
+/− collective
It then states that “any group of humans at + collective voice or higher” should be categorized as ORGANIZATION , while those below should simply be annotated as HUMAN . By listing properties that a group must have to count as an organization in the sense of the annotation scheme, the decision is simplified considerably, and by providing a decision procedure, the number of unclear cases is reduced. The annotation scheme also illustrates the use of the hierarchy:
Thus, while “the posse” would be an org, “the mob” might not be, depending on whether we see the mob as having a collective purpose. “The crowd” would not be considered org, but rather simply HUMAN .
Whether or not to include such specific examples is a question that must be answered in the context of particular research projects. One advantage is that examples may help the annotators understand the annotation scheme. A disadvantage is that examples may be understood as prototypical cases against which the referents in the data are to be matched, which may lead annotators to ignore the definitions and decision procedures.
The third step, discussed in detail in the next section, consists in testing the reliability of our annotation scheme. When we are satisfied that the scheme can be reliably applied to the data, the final step is the annotation itself.
4.2.3 The reliability of annotation schemes
In some cases, we may be able to define our variables in such a way that they can be annotated automatically. For example, if we define WORD LENGTH in terms of “number of letters”, we could write a simple computer program to go through our corpus, count the letters in each word and attach the value as a tag. Since computers are good at counting, it would be easy to ensure that such a program is completely accurate in its decisions. We could also, for example, create a list of the 2500 most frequent nouns in English and their ANIMACY values, and write a program that goes through a tagged corpus and, whenever it encounters a word tagged as a noun, looks up this value and attaches it to the word as a tag. In this case, the accuracy is likely to be much lower, as the program would not be able to distinguish between different word senses, for example assigning the label ANIMATE to the word horse regardless of whether it refers to an actual horse, a hobby horse (which should be annotated as INANIMATE or ABSTRACT depending on whether an actual or a metaphorical hobby horse is referred to) or whether it occurs in the idiom STRAIGHT FROM THE HORSE'S MOUTH (where it would presumably have to be annotated as HUMAN, if at all).
In these more complex cases, we can, and should, assess the quality of the automatic annotation in the same way in which we would assess the quality of the results returned by a particular query, in terms of precision and recall (cf. Section 4.1.2, Table 4.1). In the context of annotating data, a true positive result for a particular value would be a case where that value has been assigned to a corpus example correctly, a false positive would be a case where that value has been assigned incorrectly, a false negative would be a case where the value has not been assigned although it should have been, and a true negative would be a case where the value has not been assigned and should not have been assigned.
This assumes, however, that we can determine with a high degree of certainty what the correct value would be in each case. The examples discussed in this chapter show, however, that this decision itself often involves a certain degree of interpretation – even an explicit and detailed annotation scheme has to be applied by individuals based on their understanding of the instructions contained in it and the data to which they are to be applied. Thus, a certain degree of subjectivity cannot be avoided, but we need to minimize the subjective aspect of interpretation as much as possible.
The most obvious way of doing this is to have (at least) two different annotators apply the annotation scheme to the data – if our measurements cannot be made objective (and, as should be clear by now, they rarely can in linguistics), this will at least allow us to ensure that they are intersubjectively reliable.
One approach would be to have the entire data set annotated by two annotators independently on the basis of the same annotation scheme. We could then identify all cases in which the two annotators did not assign the same value and determine where the disagreement came from. Obvious possibilities include cases that are not covered by the annotation scheme at all, cases where the definitions in the annotation scheme are too vague to apply or too ambiguous to make a principled decision, and cases where one of the annotators has misunderstood the corpus example or made a mistake due to inattention. Where the annotation scheme is to blame, it could be revised accordingly and re-applied to all unclear cases. Where an annotator is at fault, they could correct their annotation decision. At the end of this process we would have a carefully annotated data set with no (or very few) unclear cases left.
However, in practice there are two problems with this procedure. First, it is extremely time-consuming, which will often make it difficult to impossible to find a second annotator. Second, discussing all unclear cases but not the apparently clear cases holds the danger that the former will be annotated according to different criteria than the latter.
Both problems can be solved (or at least alleviated) by testing the annotation scheme on a smaller dataset using two annotators and calculating its reliability across annotators. If this so-called interrater reliability is sufficiently high, the annotation scheme can safely be applied to the actual data set by a single annotator. If not, it needs to be made more explicit and applied to a new set of test data by two annotators; this process must be repeated until the interrater reliability is satisfactory.
A frequently used measure of interrater reliability in designs with two annotators is Cohen’s K (Cohen 1960), which can range from 0 (“no agreement”) to 1 (“complete agreement”). It is calculated as shown in (22): 4
(22)
In this formula, po is the relative observed agreement between the raters (i.e. the percentage of cases where both raters have assigned the same category) and p e is the relative expected agreement (i.e. the percentage of cases where they should have agreed by chance).
Table 4.3 shows a situation where the two raters assign one of the two categories x or y. Here, p o would be the sum of n (x, x) and n (y, y), divided by the sum of all annotation; p e can be calculated in various ways, a straightforward one will be introduced below.
Table 4.3: A contingency table for two raters and two categories
Let us look at a concrete case. In English, many semantic relations can be expressed by more than one linguistic structure. A prominent example is ‘possession’, which can be expressed in two main ways: either by the s -possessive (traditionally referred to as “genitive”) with the modifier marked by the clitic ’s (cf. 23a), or by the of -construction, with the modifier as a prepositional object of the preposition of (cf. 23b):
(23) a. a tired horse’s plodding step (BROWN K13)
b. every leaping stride of the horse (BROWN N02)
Let us assume that we want to investigate the factors determining the choice between these two constructions (as we will do in Chapters 5 and 6). In order to do so, we need to identify the subset of constructions with of that actually correspond to the s-possessive semantically – note that the of -construction encodes a wide range of relations, including many – for example quantification or partition – that are never expressed by an s -possessive. This means that we must manually go through the hits for the of -construction in our data and decide whether the relation encoded could also be encoded by an s -possessive. Let us use the term “ of -possessive” for these cases. Ideally, we would do this by searching a large corpus for actual examples of paraphrases with the s -possessive, but let us assume that this is too time-consuming (a fair assumption in many research contexts) and that we want to rely on introspective judgments instead.
We might formulate a simple annotation scheme like the following:
For each case of the structure [(DET i ) (AP i ) N i of NP j ], paraphrase it as an s -possessive of the form [NP j ’s (AP i ) N i ] (for example, the leaping stride of the horse becomes the horse’s leaping stride ). If the result sounds like something a speaker of English would use, assign the label POSS, if not, assign the label other .
In most cases, following this instruction should yield a fairly straightforward response, but there are more difficult cases. Consider (24a, b) and (25a, b), where the paraphrase sounds decidedly odd:
(24) a. a lack of unity
b. ?? unity’s lack
(25) a. the concept of unity
b. ?? unity’s concept
At first glance, neither of them seems to be paraphraseable, so they would both be assigned the value OTHER according to our annotation scheme. However, in a context that strongly favors s -possessives – namely, where the possessor is realized as a possessive determiner –, the paraphrase of (24a) sounds acceptable, while (25a) still sounds odd:
(26) a. Unity is important, and its lack can be a problem.
b. ?? Unity is important, so its concept must be taught early.
Thus, we might want to expand our annotation scheme as follows:
For each case of the structure [(DET i ) (AP i ) N i of NP j ],
- paraphrase it as an s -possessive of the form [NP j ’s (AP i ) N i ] (for example, the leaping stride of the horse becomes the horse’s leaping stride ). If the result sounds like something a speaker of English would use, assign the label POSS. If not,
- replace NP j by a possessive determiner (for example, the horse’s leaping stride becomes its leaping stride and construct a coordinated sentence with NP j as the subject of the first conjunct and [PDET j (AP i , ) N i ] as the subject of the second conjunct (for example, The horse tired and its leaping stride shortened . If the s -possessive sounds like something a speaker of English would use in this context, assign the label poss,) if not, assign the label OTHER.
Obviously, it is no simple task to invent a meaningful context of the form required by these instructions and then deciding whether the result is acceptable. In other words, it is not obvious that this is a very reliable operationalization of the construct OF -POSSESSSIVE, and it would not be surprising if speakers’ introspective judgments varied too drastically to yield useful results.
Table 4.4 shows a random sample of of -constructions from the BROWN corpus (with cases that have a quantifying noun like lot or bit instead of a regular noun as N i already removed. The introspective judgments were derived by two different raters (both trained linguists who wish to remain anonymous) based on the instructions above. The tabulated data for both annotators are shown in Table 4.5.
As discussed above, the relative observed agreement is the percentage of cases both raters chose POSS or both raters chose OTHER, i.e.
The relative expected agreement, i.e. the probability that both raters agree in their choice between POSS or OTHER by chance, can be determined as follows (we will return to this issue in the next chapter and keep it simple here). RATER 1 chose POSS with a probability of 20 / 30 = 0.6667 (i.e., 66.67 percent of the time) and OTHER with a probability of 10 / 30 = 0.3333 (i.e., 33.33 percent of the time). RATER 2 chose POSS with a probability of 19 / 30 = 0.6333, and OTHER with a probability of 11 / 30 = 0.3667.
The joint probability that both raters will choose poss by chance is the product of their individual probabilities of doing so, i.e. 0.6667 × 0.6333 = 0.4222; for no it is 0.3333 × 0.3667 = 0.1222. Adding these two probabilities gives us the overall probability that the raters will agree by chance: 0.4222 + 0.1222 = 0.5444.
Table 4.4: Paraphraseablility ratings for a sample of of -constructions by two raters
Table 4.5: Count of judgments from Table 4.4
We can now calculate the interrater reliability for the data in Table 4.4 using the formula in (22) above:
There are various suggestions as to what value of K is to be taken as satisfactory. One reasonable suggestion (following McHugh 2012) is shown in Table 4.6; according to this table, our annotation scheme is good enough to achieve “strong” agreement between raters, and hence presumably good enough to use in a corpus linguistic research study (what is “good enough” obviously depends on the risk posed by cases where there is no agreement in classification).
Table 4.6: Interpretation of K values
4.2.4 Reproducibility
Scientific research is collaborative and incremental in nature, with researchers building on and extending each other’s work. As discussed in the previous chapter in Section 3.3, this requires that we be transparent with respect to our data and methods to an extent that allows other researchers to reproduce our results. This is referred to as reproducibility and/or (with a different focus, replicability ) – since there is some variation in how these terms are used, let us briefly switch to more specific, non-conventional terminology.
The minimal requirement of an incremental and collaborative research cycle is what we might call retraceability : our description of the research design and the associated procedures must be explicit and detailed enough for another researcher to retrace and check for correctness each step of our analysis and when provided with all our research materials (i.e., the corpora, the raw extracted data and the annotated data) and all other resources used (such as our annotation scheme and the software used in the extraction and statistical analysis of the data) and all our research notes, intermediate calculations, etc. In other words, our research project must be documented in sufficient detail for others to make sure that we arrived at our results via the procedures that we claim to have used, and to identify possible problems in our data and/or procedure. Thus retraceability is closely related to the idea of accountability in accounting.
Going slightly beyond retraceability, we can formulate a requirement which we might call reconstructibility : given all materials and resources, the description of our procedure must be detailed enough to ensure that a researcher independently applying this procedure to the same data using the same resources, but without access to our research notes, intermediate results, etc., should arrive at the same result.
Both of the requirements just described fall within the range of what is referred to as reproducibility. Obviously, as long as our data and other resources (and, if relevant, our research notes and intermediate results) are available, reproducibility is largely a matter of providing a sufficiently explicit and fine-grained description of the steps by which we arrived at our results. However, in actual practice it is surprisingly difficult to achieve reproducibility even if our research design does not involve any manual data extraction or annotation (see Stefanowitsch (2018) for an attempt). As soon as our design includes steps that involve manual extraction or annotation, it may still be retraceable, but it will no longer be reconstructible; however, if we ensure that our extraction methods and annotation scheme(s) have a high interrater reliability, an attempt at reconstruction should at least lead to very similar results.
Matters become even more difficult if our data or other resources are not accessible, for example, if we use a corpus or software constructed specifically for our research project that we cannot share publicly due to copyright restrictions, or if our corpus contains sensitive information such that sharing it would endanger individuals, violate non-disclosure agreements, constitute high treason, etc.
In such a case, our research will not be reproducible (retraceable or reconstructible), which is why we should avoid this situation. If it cannot be avoided, however, our research should still meet a requirement that we might call transferability : the description of our materials, resources and procedures must be detailed enough for other researchers to transfer it to similar materials and resources and arrive at a similar result. This is broadly what replicability refers to, but the term may also be used in an extended sense to situations where a researcher attempts to replicate our results and conclusions rather than our entire research design. Obviously, research designs that meet the criterion of reproducibility are also transferable, but not vice versa.
In the context of the scientific research cycle, reproducibility and replicability are crucial: a researcher building on previous research must be able to reconstruct this research in order to determine its quality and thus the extent to which our results can be taken to corroborate our hypotheses. They must also be able to reconstruct the research in order to build on it in a meaningful way, for example by adapting our methods to other kinds of data, new hypotheses or other linguistic phenomena.
Say, for example, a researcher wanted to extend the analysis of the words wind-screen and windshield presented in Chapter 3, Section 3.1 to other varieties of English. The first step would be to reconstruct our analysis (if they had access to the LOB, BROWN, FLOB and FROWN corpora), or to adapt it (for example, to the BNC and COCA, briefly mentioned at the end of our analysis). However, with the information provided in Chapter 3, this would be very difficult. First, there was no mention of which version of the LOB, BROWN, FLOB and FROWN was used (there are at least two official releases, one that was commercially available from an organization called ICAME and a different one that is available via the CLARIN network, and each of those releases contains different versions of each corpus). Second, there was no mention of the exact queries used – obviously, the words can be spelled in a range of ways, including at least WINDSCREEN , WIND SCREEN , WIND-SCREEN , Windscreen , Wind screen , Wind Screen , Wind-screen , Wind-Screen , windscreen , wind screen and wind-screen for WINDSCREEN, and the corresponding variants for WINDSHIELD. This range of graphemic variants can be searched for in different ways depending what annotation the respective version of the corpus contains, what software was used to access it and how we want to formulate our query. None of this information was provided in Chapter 3. Finally, nothing was said about how the total number of hits was calculated, which is not a problem in the case of a simple mathematical operation like addition, but which can quickly become relevant in the case of procedures and software used to evaluate the results statistically (as discussed in the next chapter).
It would not be surprising if a researcher attempting to reconstruct our analysis would get different results. This is not a purely theoretical possibility even with such a simple research design – you will often find that even word frequencies reported for a given corpus in the literature will not correspond to what your own query of the same corpus yields. Obviously, the more complex the phenomenon, the more difficult it will be to guess what query or queries a researcher has used if they do not tell us. And if the data had to be annotated in any way more complex than “number of letters”, that annotation will be difficult to reconstruct even if an annotation scheme is provided, and impossible to reconstruct if this is not the case.
Unfortunately, corpus linguists have long paid insufficient attention to this (and I include much of my own research in this criticism). It is high time that this change and that corpus linguists make an honest effort to describe their designs in sufficient detail to make them reproducible (in all senses discussed above). In many disciplines, it is becoming customary to provide raw data, categorized data, computer scripts, etc. in the form of supplementary materials with every research article. This is not yet standard in corpus linguistics, but it is a good idea to plan and document your research as though it already were.
4.2.5 Data storage
We will conclude this chapter with a discussion of a point that may, at first, appear merely practical but that is crucial in carrying out corpus-linguistic research (and that has some methodological repercussions, too): the question of how to store our data and annotation decisions. There are broadly two ways of doing so: first, in the corpus itself, and second, in a separate database of some sort.
The first option is routinely chosen in the case of automatically annotated variables like PART OF SPEECH, as in the passage from the BROWN corpus cited in Figure 2.4 in Chapter 2, repeated here partially as (27):
(27) the_AT fact_NN that_CS Jess's_NP$ horse_NN had_HVD not_*
been_BEN returned_VBN to_IN its_PP$ stall_NN could_MD
indicate_VB that_CS Diane's_NP$ information_NN had_HVD been_BEN wrong_JJ ,_, but_CC Curt_NP didn't_DOD* interpret_VB
it_PPO this_DT way_NN ._. (BROWN N12)
Here, the annotation (i.e., the part-of-speech tags) are attached to the data they refer to (i.e., words) by an underscore (recall that alternatively, a vertical format with words in the first and tags in the second column is frequently used, as are various kinds of XML notation).
The second option is more typically chosen in the case of annotations added in the context of a specific research project (especially if they are added manually): the data are extracted, stored in a separate file, and then annotated. Frequently, spreadsheet applications are used to store the corpus data and annotation decisions, as in the example in Figure 4.1, where possessive pronouns and nouns are annotated for NOMINAL TYPE, ANIMACY and CONCRETENESS:
Figure 4.1: Example of a raw data table
The first line contains labels that tell us what information is found in each column respectively. This should include the example itself (either as shown in Figure 4.1, or in the form of a KWIC concordance line) and meta-information such as what corpus and/or corpus file the example was extracted from. Crucially, it will include the relevant variables. Each subsequent line contains one observation (i.e., one hit and the appropriate values of each variable). This format – one column for each variable, and one line for each example – is referred to as a raw data table . It is the standard way of recording measurements in all empirical sciences and we should adhere to it strictly, as it ensures that the structure of the data is retained.
In particular, we should never store our data in summarized form, for example, as shown in Figure 4.2.
Figure 4.2: Data stored in summarized form
There is simply no need to store data in this form – if we need this kind of summary, it can be created automatically from a raw data table like that in Figure 4.1 – all major spreadsheet applications and statistical software packages have this functionality. What is more, statistics software packages require a raw data table of the kind shown in Figure 4.1 as input for most kinds of statistical analysis.
As mentioned above, however, the format of storage is not simply a practical matter, but a methodological one. If we did store our data in the form shown in Figure 4.2 straight away, we would irreversibly destroy the relationship between the corpus hits and the different annotations applied to them. In other words, we would have no way of telling which combinations of variables actually occurred in the data. In Figure 4.2, for example, we cannot tell whether the pronoun referred to one one of the human referents or to the animate referent. Even if we do not need to know these relationships in the context of a given research project (or initially believe we will not), we should avoid this situation, as we do not know what additional questions may come up in the course of our research that do require this information.
Since quantitative analysis always requires a raw data table, we might conclude that it is the only useful way of recording our annotation decisions. However, there are cases where it may be more useful to record them in the form of annotations in (a copy of) the original corpus instead, i.e., analogously to automatically added annotations. For example, the information in Figure 4.1 could be recorded in the corpus itself in the same way that part-of-speech tags are, i.e., we could add an ANIMACY label to every nominal element in our corpus in the format used for POS tags by the original version of the BROWN corpus, as shown in (28):
(28) the_AT fact_NN_abstract that_CS Jess's_NP$_human horse_NN_animate had_HVD not_* been_BEN returned_VBN to_IN its_PP$_animate stall_NN_inanimate could_MD indicate_VB that_CS Diane's_NP$_human information_NN_abstract had_HVD been_BEN wrong_JJ ,_, but_CC Curt_NP_human didn't_DOD* interpret_VB it_PPO_inanimate this_DT way_NN_inanimate ._.
From a corpus annotated in this way, we can always create a raw data list like that in Figure 4.1 by searching for possessives and then separating the hits into the word itself, the PART-OF-SPEECH label and the ANIMACY annotation (this can be done manually, or with the help of regular expressions in a text editor or with a few lines of code in a scripting language like Perl or Python).
The advantage would be that we, or other researchers, could also use our annotated data for research projects concerned with completely different research questions. Thus, if we are dealing with a variable that is likely to be of general interest, we should consider the possibility of annotating the corpus itself, instead of first extracting the relevant data to a raw data table and annotating them afterwards. While the direct annotation of corpus files is rare in corpus linguistics, it has become the preferred strategy in various fields concerned with qualitative analysis of textual data. There are open-source and commercial software packages dedicated to this task. They typically allow the user to define a set of annotation categories with appropriate codes, import a text file, and then assign the codes to a word or larger textual unit by selecting it with the mouse and then clicking a button for the appropriate code that is then added (often in XML format) to the imported text. This strategy has the additional advantage that one can view one’s annotated examples in their original context (which may be necessary when annotating additional variables later). However, the available software packages are geared towards the analysis of individual texts and do not let the user to work comfortably with large corpora.
3 In fact, it may be worth exploring, within a corpus-linguistic framework, ways of annotating data that are based entirely on implicit decisions by untrained speakers; specifically, I am thinking here of the kinds of association tasks and sorting tasks often used in psycholinguistic studies of word meaning.
4 For more than two raters, there is a more general version of this metric, referred to as Fleiss’ K (Fleiss 1971), but as it is typically difficult even to find a second annotator, we will stick with the simpler measure here.


45.E: Population and Community Ecology (Exercises)
45.1: Population Demography
Populations are dynamic entities. Populations consist all of the species living within a specific area, and populations fluctuate based on a number of factors: seasonal and yearly changes in the environment, natural disasters such as forest fires and volcanic eruptions, and competition for resources between and within species. The statistical study of population dynamics, demography, uses a series of mathematical tools to investigate how populations respond to changes in their environments.
Review Questions
Which of the following methods will tell an ecologist about both the size and density of a population?
- mark and recapture
- mark and release
- quadrat
- life table
- Answer
-
C
Which of the following is best at showing the life expectancy of an individual within a population?
- quadrat
- mark and recapture
- survivorship curve
- life table
- Answer
-
D
Humans have which type of survivorship curve?
- Type I
- Type II
- Type III
- Type IV
- Answer
-
A
Free Response
Describe how a researcher would determine the size of a penguin population in Antarctica using the mark and release method.
- Answer
-
The researcher would mark a certain number of penguins with a tag, release them back into the population, and, at a later time, recapture penguins to see what percentage of the recaptured penguins was tagged. This percentage would allow an estimation of the size of the penguin population.
45.2: Life Histories and Natural Selection
A species’ life history describes the series of events over its lifetime, such as how resources are allocated for growth, maintenance, and reproduction. Life history traits affect the life table of an organism. A species’ life history is genetically determined and shaped by the environment and natural selection.
Review Questions
Which of the following is associated with long-term parental care?
- few offspring
- many offspring
- semelparity
- fecundity
- Answer
-
A
Which of the following is associated with multiple reproductive episodes during a species’ lifetime?
- semiparity
- iteroparity
- semelparity
- fecundity
- Answer
-
B
Which of the following is associated with the reproductive potential of a species?
- few offspring
- many offspring
- semelparity
- fecundity
- Answer
-
D
Free Response
Why is long-term parental care not associated with having many offspring during a reproductive episode?
- Answer
-
Parental care is not feasible for organisms having many offspring because they do not have the energy available to take care of offspring. Most of their energy budget is used in the formation of seeds or offspring, so there is little left for parental care. Also, the sheer number of offspring would make individual parental care impossible.
45.3: Environmental Limits to Population Growth
Although life histories describe the way many characteristics of a population (such as their age structure) change over time in a general way, population ecologists make use of a variety of methods to model population dynamics mathematically. These more precise models can then be used to accurately describe changes occurring in a population and better predict future changes.
Review Questions
Species with limited resources usually exhibit a(n) ________ growth curve.
- logistic
- logical
- experimental
- exponential
- Answer
-
A
The maximum rate of increased characteristic of a species is called its ________.
- limit
- carrying capacity
- biotic potential
- exponential growth pattern
- Answer
-
C
The population size of a species capable of being supported by the environment is called its ________.
- limit
- carrying capacity
- biotic potential
- logistic growth pattern
- Answer
-
B
Free Response
Describe the rate of population growth that would be expected at various parts of the S-shaped curve of logistic growth.
- Answer
-
In the first part of the curve, when few individuals of the species are present and resources are plentiful, growth is exponential, similar to a J-shaped curve. Later, growth slows due to the species using up resources. Finally, the population levels off at the carrying capacity of the environment, and it is relatively stable over time.
45.4: Population Dynamics and Regulation
The logistic model of population growth, while valid in many natural populations and a useful model, is a simplification of real-world population dynamics. Implicit in the model is that the carrying capacity of the environment does not change, which is not the case. The carrying capacity varies annually: for example, some summers are hot and dry whereas others are cold and wet. In many areas, the carrying capacity during the winter is much lower than it is during the summer.
Review Questions
Species that have many offspring at one time are usually:
- r -selected
- K -selected
- both r - and K -selected
- not selected
- Answer
-
A
A forest fire is an example of ________ regulation.
- density-dependent
- density-independent
- r -selected
- K -selected
- Answer
-
B
Primates are examples of:
- density-dependent species
- density-independent species
- r -selected species
- K -selected species
- Answer
-
D
Free Response
Give an example of how density-dependent and density-independent factors might interact.
- Answer
-
If a natural disaster such as a fire happened in the winter, when populations are low, it would have a greater effect on the overall population and its recovery than if the same disaster occurred during the summer, when population levels are high.
45.5: Human Population Growth
Although humans have increased the carrying capacity of their environment, the technologies used to achieve this transformation have caused unprecedented changes to Earth’s environment, altering ecosystems to the point where some may be in danger of collapse. The depletion of the ozone layer, erosion due to acid rain, and damage from global climate change are caused by human activities. The ultimate effect of these changes on our carrying capacity is unknown.
Review Questions
A country with zero population growth is likely to be ________.
- in Africa
- in Asia
- economically developed
- economically underdeveloped
- Answer
-
D
Which type of country has the greatest proportion of young individuals?
- economically developed
- economically underdeveloped
- countries with zero population growth
- countries in Europe
- Answer
-
B
Which of the following is not a way that humans have increased the carrying capacity of the environment?
- agriculture
- using large amounts of natural resources
- domestication of animals
- use of language
- Answer
-
B
Free Response
Describe the age structures in rapidly growing countries, slowly growing countries, and countries with zero population growth.
- Answer
-
Rapidly growing countries have a large segment of the population at a reproductive age or younger. Slower growing populations have a lower percentage of these individuals, and countries with zero population growth have an even lower percentage. On the other hand, a high proportion of older individuals is seen mostly in countries with zero growth, and a low proportion is most common in rapidly growing countries.
45.6: Community Ecology
Populations rarely, if ever, live in isolation from populations of other species. In most cases, numerous species share a habitat. The interactions between these populations play a major role in regulating population growth and abundance. All populations occupying the same habitat form a community: populations inhabiting a specific area at the same time. The number of species occupying the same habitat and their relative abundance is known as species diversity.
Review Questions
The first species to live on new land, such as that formed from volcanic lava, are called ________.
- climax community
- keystone species
- foundation species
- pioneer species
- Answer
-
D
Which type of mimicry involves multiple species with similar warning coloration that are all toxic to predators?
- Batesian mimicry
- Müllerian mimicry
- Emsleyan/Mertensian mimicry
- Mertensian mimicry
- Answer
-
B
A symbiotic relationship where both of the coexisting species benefit from the interaction is called ________.
- commensalism
- parasitism
- mutualism
- communism
- Answer
-
C
Free Response
Describe the competitive exclusion principle and its effects on competing species.
- Answer
-
The competitive exclusion principle states that no two species competing for the same resources at the same time and place can coexist over time. Thus, one of the competing species will eventually dominate. On the other hand, if the species evolve such that they use resources from different parts of the habitat or at different times of day, the two species can exist together indefinitely.
45.7: Behavioral Biology: Proximate and Ultimate Causes of Behavior
One goal of behavioral biology is to dissect out the innate behaviors, which have a strong genetic component and are largely independent of environmental influences, from the learned behaviors, which result from environmental conditioning. Innate behavior, or instinct, is important because there is no risk of an incorrect behavior being learned. They are “hard wired” into the system. On the other hand, learned behaviors, although riskier, are flexible, dynamic, and can be altered.
Review Questions
The ability of rats to learn how to run a maze is an example of ________.
- imprinting
- classical conditioning
- operant conditioning
- cognitive learning
- Answer
-
D
The training of animals usually involves ________.
- imprinting
- classical conditioning
- operant conditioning
- cognitive learning
- Answer
-
C
The sacrifice of the life of an individual so that the genes of relatives may be passed on is called ________.
- operant learning
- kin selection
- kinesis
- imprinting
- Answer
-
B
Free Response
Describe Pavlov’s dog experiments as an example of classical conditioning.
- Answer
-
Dogs salivated in response to food. This was the unconditioned stimulus and response. Dogs exposed to food had a bell rung repeatedly at the same time, eventually learning to associate the bell with food. Over time, the dogs would salivate when the bell was rung, even in the absence of food. Thus, the bell became the conditioned stimulus, and the salivation in response to the bell became the conditioned response.