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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".