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Record W2754603463 · doi:10.1111/lang.12254

Talking It Through: Collaborative Dialogue and Second Language Learning

2017· article· en· W2754603463 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueLanguage Learning · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversité de Montréal
FundersFonds de Recherche du Québec-Société et Culture
KeywordsDictationPsychologyIntervention (counseling)LinguisticsLanguage proficiencySecond languageMathematics education

Abstract

fetched live from OpenAlex

Abstract This quasi‐experimental study examined the potential benefits of zero‐error dictation, a teaching technique based on dialogue‐driven dictation, and the extent to which target linguistic features and proficiency levels mediated its effects on second language (L2) learners’ accurate use of French grammatical morphology. Seventy‐nine learners of L2 French in four Grade 5 and 6 classes in the Montreal area were assigned to two conditions: traditional dictation (comparison) group and zero‐error dictation (experimental) group. The instructional intervention, which consisted of five sessions spread over a 5‐week period, targeted four different morphological features. Learners’ knowledge of the target features was tested immediately before the experimental intervention started and immediately after it ended. Findings indicated that the experimental treatment group outperformed the comparison group and that learners’ gain scores varied across the target structures and learner proficiency levels.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.575
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.280
Teacher spread0.261 · 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