Bibliographic record
Abstract
The term ‘bidialectal’ is used in the academic—and to some extent popular—literature to refer to an individual’s command over two dialects (D1 and D2) of a given language (see Siegel, 2010). However, this term subsumes two broad categories of competence in said dialects, namely that of the phonology on the one hand, and of lexicon, morphology and idiomatic usage (‘lexicon’) on the other. Studies in Second Dialect Acquisition (SDA) are themselves few in number, and the few that exist do not always clearly distinguish between phonological and lexical acquisition. For instance, two of the earliest studies in the field (Labov, 1972; Wells, 1973) both deal with the acquisition of a second phonology, but do not refer to it as such, instead including it under ‘bidialectalism’. More recently, a well-known paper in the field (Tagliamonte & Molfenter, 2007) entitled “How’d you get that accent?: Acquiring a second dialect of the same language” actually deals with the acquisition of a second phonology, namely British English phonology by Canadian-English-speaking children. The term ‘dialect’ in the title therefore could be construed as indicative of a wider scope of study than the paper actually covers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.104 | 0.027 |
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; both teacher heads agree on what is shown here.
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".