Chapter 6 considers the scribes’ copying of the text itself in the fifteenth century, especially in works by Thomas Hoccleve and Geoffrey Chaucer and spurious lines added to Chaucer’s poetry. It queries the assumption that scribes vary the text a lot, and in three quantitative samples it suggests that, despite the difficulties of transcription and the acceptability of revision, scribes could copy exemplars very closely. As a result, despite the material differences between books, what most distinguished them was verbal likeness. It suggests that this reproduction reflects an interest in the text’s survival as an immaterial, verbal artefact, partly out of respect for certain kinds of English poetry, and a disregard for its material form.

We emphasize that the ability for a corpus to provide accurate estimates of a linguistic parameter depends on the combined influence of domain considerations (coverage bias and selection bias) and distribution considerations (corpus size). By using a series of experimental corpora on the domain of Wikipedia articles, we can demonstrate the impact of corpus size, coverage bias, selection bias, and stratification on representativeness. Empirical results show that robust sampling methods and large sample sizes can only give you a better representation of the operational domain (i.e. overcome selection bias). However, by themselves, these factors cannot help you achieve accurate quantitative-linguistic analyses for the actual domain (i.e. overcome coverage bias) Uncontrolled domain considerations can lead to unpredictable results with respect to accuracy.

We define a linguistic distribution as the range of values for a quantitative linguistic variable across the texts in a corpus. An accurate parameter estimate means that the measures based on the corpus are close to the actual values of a parameter in the domain. Precision refers to whether or not the corpus is large enough to reliably capture the distribution of a particular linguistic feature. Distribution considerations relate to the question of how many texts are needed. The answer will vary depending on the nature of the linguistic variable of interest. Linguistic variables can be categorized broadly as linguistic tokens (rates of occurrence for a feature) and linguistic types (the number of different items that occur). The distribution considerations for linguistic tokens and linguistic types are fundamentally different. Corpora can be “undersampled” or “oversampled” – neither of which is desirable. Statistical measures can be used to evaluate corpus size relative to research goals – one set of measures enables researchers to determine the required sample size for a new corpus, while another provides a means to determine precision for an existing corpus. The adage “bigger is better” aptly captures our best recommendation for studies of words and other linguistic types.

The discipline of “diplomatics” – originating in the seventeenth century to systematically test the authenticity of medieval documents – has more recently been adapted to the study of digital records and their systems. In establishing the necessary elements for the long-term preservation of authentic records, archival diplomatics provides one possible (and powerful) analytic framework and methodology for analyzing the trustworthiness of records, including those to be found in blockchain and distributed ledgers. Regardless of the type of blockchain and distributed ledger system under examination, each relies upon trust in the ledger and in the records the ledger contains. Yet each type of blockchain and distributed ledger system still has limitations when judged against archival diplomatic standards of records’ trustworthiness, which demands the accuracy, reliability, and authenticity of records. By gaining an understanding of the elemental requirements for trust in records (and in record systems), there is hope that the designers of blockchain and distributed ledger systems might continue to improve the evidentiary quality of blockchain records and recordkeeping.

This chapter presents a general overview of sensor characterization from a system perspective, without any reference to a specific implementation. The systems are defined on the basis of input and output signal description and the overall architecture is discussed, showing how the information is transduced, limited, and corrupted by errors. One of the main points of this chapter is the characterization of the error model, and how this one could be used to evaluate the uncertainty of the measure, along with its relationship with resolution, precision and accuracy of the overall system. Finally, the quantization process, which is at the base of any digital sensor systems, is illustrated, interpreted, and included in the error model.

A core claim of big-data-algorithm enthusiasts – producers, champions, consumers – is that big-data algorithms are able to deliver insightful and accurate predictions about human behaviour. This chapter challenges this claim. I make three contributions: First, I perform a conceptual analysis and argue that big-data analytics is by design a-theoretical and does not provide process-based explanations of human behaviour, making it unfit to support insight and deliberation, which is transparent to both legal experts and non-experts. Second, I review empirical evidence from dozens of data sets, which suggests that the predictive accuracy of mathematically sophisticated algorithms is not consistently higher than that of simple rules (rules that tap on available domain knowledge or observed human decision-making); rather, big-data algorithms are less accurate across a range of problems, including predicting election results and criminal profiling (this work presented here refer to understanding and predicting human behaviour in legal and regulatory contexts). Third, I synthesize the above points in order to conclude that simple, process-based, domain-grounded theories of human behaviour should be put forth as benchmarks, which big-data algorithms, if they are to be considered as tools for personalization, should match in terms of transparency and accuracy.

As an empirical science, the study of animal behaviour involves measurement. When an animal engages in a series of actions, such as exploring or catching prey, the problem becomes that of identifying suitable components from that stream of action to use as markers suitable to score. The markers selected reflect the observer’s hypothesis of the organisation of the behaviour. Unfortunately, in most cases, researchers only provide heuristic descriptions of what they measure. To make the study of animal behaviour more scientific, the hypotheses underlying the decision of what to measure should be made explicit so as to allow them to be tested. Using hypothesis testing as a guiding framework, several principles that have been shown to be useful in identifying behavioural organisation are presented, providing a starting point in deciding what markers to select for measurement.

Poor-quality measurements are likely to yield meaningless or unrepeatable findings. High-quality measurements are characterised by validity and reliability. Validity relates to whether the right quantity is measured and is assessed by comparing a metric with a gold-standard metric. Reliability relates to whether measurements are repeatable and is assessed by comparing repeated measurements. The accuracy and precision with which measurements are made affect both validity and reliability. A major source of unreliability in behavioural data comes from the involvement of human observers in the measurement process. Where trade-offs are necessary, it is better to measure the right quantity somewhat unreliably than to measure the wrong quantity very reliably. Floor and ceiling effects can make measurements useless for answering a question, even if they are valid and reliable. Outlying data points should only be removed if they can be proved to be biologically impossible or to result from errors.

This chapter reviews themes in research into the effectiveness of oral corrective feedback, typically provided by language teachers, on L2 grammatical development. It synthesizes research evidence for the effects of oral corrective feedback on learners’ development of grammar and the relative efficacy of different corrective feedback strategies, such as output-prompting and input-providing. Further themes concern the effectiveness of oral corrective feedback on salient and non-salient grammatical features and in relation to learners’ varying levels of knowledge of the targeted features. Even though most research in this area concerns the development of accuracy, the chapter includes a review of the considerably smaller body of literature that offers insights into the potential value of oral corrective feedback on the development of fluency. The chapter reviews the different kinds of oral and written tests that have been used in research to gauge grammar learning, some of which teachers may wish to consider adopting to assess their learners. Based on the cumulative evidence from research, we make suggestions for classroom teachers, although we recognize that teachers’ decisions about the provision of oral corrective feedback are often based on multiple factors, including affective factors and teaching objectives.

While written language development involves reducing erroneous expressions, traditional error-based measures are problematic for several reasons, including low inter-coder reliability for lexical errors, limited sensitivity for capturing development within a short time period, and the questionable separation of lexical and grammatical errors. Given these problems, we explore automated accuracy measures rooted in a usage-based theory of Second Language Acquisition, which views language as a set of constructions or chunks. For this study, we examined 139 essays in terms of using traditional measures of complexity, accuracy, lexical sophistication, and fluency, as well as novel corpus-based n-gram measures. A factor analysis was conducted to explore how traditional measures grouped with corpus-based measures, and regression analyses were used to examine how corpus-based measures predicted error counts and holistic accuracy scores. With the results of these analyses, we suggest that automated n-gram based measures are a viable alternative to traditional accuracy measures.