An Innovative Method in Methods? Experimental Tasks in Regional Variation
Bibliographic record
Abstract
Labov (1972a, 1972b) criticized Chomsky's (1965) notions that variation is an error and that speech communities are homogeneous. He maintained that variation is not random but rather and rule-governed, and that in order to describe language used by members of a speech community it is necessary to study the vernacular, which provides the most systematic data. He further maintained that the only way to obtain sufficient good data on the speech of anyone person is through an individual, tape-recorded interview (1972b: 181). Since then, the sociolinguistic interview has been the preferred source of data in sociolinguistic research (Milroy 1987; Coulmas 1997; Feagin 2001). Yet Labov also acknowledged that there are a number of problems associated with studying spontaneous speech, including the rarity of certain forms (l972a:188). Moreover, an enormous amount of work is involved in data collection, transcription and coding (Milroy 1987; Feagin 2001). In this paper I present results of a research project which employed two paper and-pencil tasks, a controlled-production task and an acceptability judgment task, to study auxiliary use and its correlates in Acadian French. These tasks made it possible to collect data from a large number of subjects while ensuring that the linguistic contexts were consistent across subjects; it was also possible to study linguistic phenomena which are rare or non-existent in oral speech.
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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 | 0.104 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".