Bibliographic record
Abstract
How do you collect data? This chapter will outline tried-and-true data collection techniques. The most fundamental challenge for sociolinguistic research is how to obtain appropriate linguistic data to analyse. But how do you actually do it? The best exemplars that exist – Labov (1972a), Milroy (1987) and Sankoff (1973, 1974) – were written in the 1960s and 1970s. Detailed individual accounts are rarely published, except in dissertation methodology chapters. Some of most memorable fieldwork tips I ever received were from chatting to sociolinguists at conferences (see also Feagin 2002: 37). In fact, fieldwork methods may be the best-kept secret of sociolinguistics. In this chapter, you will learn everything I know about how to collect data. THE BASICS The very first task is to design a sample that addresses ‘the relationship between research design and research objectives’ (Milroy 1987: 18, Milroy and Gordon 2003: 24). At the outset, a sociolinguistic project must have (at least) two parts: 1) a (socio)linguistic problem and 2) appropriate data to address it. Perhaps the consensus on good practice in this regard is to base one's sampling procedure on ‘specifiable and defensible principles’ (Chambers 2003: 46). The question is: What are these, and how to apply them? DATA COLLECTION According to Sankoff, the need for good data imposes three different kinds of decisions about data collection on the researcher: a) choosing what data to collect; b) stratifying the sample; and c) deciding on how much data to collect from how many speakers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.106 | 0.072 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".