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Record W2501754583 · doi:10.1075/lllt.23.13whi

Speeding up acquisition of his and her: Explicit L1/L2 contrasts help

2008· article· en· W2501754583 on OpenAlexaff
Joanna White

Bibliographic record

VenueLanguage learning and language teaching · 2008
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsConcordia University
Fundersnot available
KeywordsGrammaticalityPossessivePsychologyContext (archaeology)Metalinguistic awarenessCognitionCognitive styleLinguisticsCognitive psychologyMetalinguisticsMathematics educationGrammarTeaching methodVocabulary development

Abstract

fetched live from OpenAlex

This paper reviews three pedagogical intervention studies that demonstrate the effectiveness of providing pre-adolescent and adolescent learners in communicatively-oriented classrooms with explicit metalinguistic information and opportunities to use it. The studies, which target the possessive determiners (PDs), his and her, follow a pretest/posttest design and were carried out in intact treatment and comparison classes. Measures consist of grammaticality judgement, metalinguistic comment, and oral picture description tasks. A number of issues are discussed in the context of older children’s second language learning in a classroom setting. These include implementing age-appropriate instruction that takes into account learners’ cognitive and linguistic readiness for form-focused instruction, their learning style and motivation, and the context of instruction.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.012
GPT teacher head0.295
Teacher spread0.284 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2008
Admission routes1
Has abstractyes

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