Inquiries in linguistic development: in honor of Lydia White
Bibliographic record
Abstract
The focus of this collection is on important themes in L2 acquisition, the nature of grammatical systems developed by language learners in L1 acquisition, third language acquisition, and bilingualism and language attrition. The chapters present an interesting mix of theoretical contributions, overview studies, and experimental designs exploring various research questions, such as learnability and access to UG, L1 influence, the nature of initial and endstate grammars, and variability. The linguistic domains investigated are also extremely diverse: morphosyntax, phonology, the lexicon, argument realization, language processing, and interface phenomena. This book, edited and written by McGill University alumni, is intended as a tribute to Lydia White's contribution to the field of generative second language acquisition. The authors present current work on language acquisition which further investigates several themes developed by White's research. Through these state-of-the-art contributions the reader will be able to identify important new directions in which generative language acquisition is developing and expanding.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".