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Record W2395052721 · doi:10.1080/10489223.2016.1187616

Indirect positive evidence in the acquisition of a subset grammar

2016· article· en· W2395052721 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueLanguage Acquisition · 2016
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of CanadaFonds de Recherche du Québec-Société et Culture
KeywordsGrammarLinguisticsPsychologyPhrase structure rulesLanguage acquisitionSecond-language acquisitionSyntaxIndo-European languagesPhilosophy

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.346
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.280
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it