Indirect positive evidence in the acquisition of a subset grammar
Bibliographic record
Abstract
This article proposes that second language learners can use indirect positive evidence (IPE) to acquire a phonological grammar that is a subset of their L1 grammar. IPE is evidence from errors in the learner’s L1 made by native speakers of the learner’s L2. It has been assumed that subset grammars may be acquired using direct or indirect negative evidence or, in certain L1–L2 combinations, using direct positive evidence. The utility of IPE is tested by providing native speakers of English with indirect evidence of the phonotactic constraints holding of word-initial clusters in Brazilian Portuguese (BP), which are a subset of those in English. Participants were tested on the well-formedness of BP-like words, and the results indicate that approximately one-third were able to use the IPE to make appropriate BP-like judgments. This suggests that IPE may be another source of evidence that learners can use to build a grammar that is a subset of their own L1 grammar.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".