Naïve English-speaking learners’ use of indirect positive evidence
Bibliographic record
Abstract
Abstract When second language learners are faced with acquiring a grammar that is a subset of their native language grammar, direct positive evidence is unavailable. We question whether learners can instead use indirect positive evidence: evidence drawn from errors in the learner’s L1 made by native speakers of the learner’s L2. We examine if naïve English-speaking learners of Mandarin can determine from plural omission errors in Mandarin speakers’ English productions that Mandarin marks plural in a subset of conditions under which English does. Participants were exposed to indirect positive evidence via an English-medium dialogue where a native Mandarin-speaking interlocutor produced all contextually plural nouns as singulars. Subsequently, participants learnt 12 Mandarin-like nouns in singular contexts, after which their word learning was tested using both singular and plural pictures as prompts. Forty percent of participants correctly deduced that strings to which they had assigned singular interpretations were also appropriate in plural contexts. Follow-up questions revealed that they noticed the errors in the dialogue and used these to inform their understanding of plural marking in Mandarin. This result suggests that indirect positive evidence may be an effective tool for real language learners to acquire a grammar that is a subset of their native 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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".