MétaCan
Menu
Back to cohort

Learner Accuracy and Learner Performance: The Quest for a Link

2000· article· en· W1964987241 on OpenAlexfundno aff
Janet Renou

Bibliographic record

VenueForeign Language Annals · 2000
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
FundersUniversity of Ottawa
KeywordsGrammarPsychologyLanguage proficiencySignificant differenceFocus on formMode (computer interface)LinguisticsExploratory researchCognitive psychologyComputer scienceMathematics educationStatisticsMathematics

Abstract

fetched live from OpenAlex

Abstract: This paper presents the results of an exploratory study on advanced‐level French second language learners' metalinguistic awareness. Specifically, we examined learner performance in carrying out three steps of a written and oral grammatically judgment test. First, subjects' ability to identify and correct an error, and to provide the rule, which the correction entailed, was examined according to group membership (communicative or grammar), types of errors, and mode of presentation. In a second phase of the analysis, judgment ability was compared with specific aspects of L2 proficiency. Results show significant differences between the groups in their ability to provide the rule that the correction entailed. Furthermore, significant differences in judgment ability were found depending on whether the item was presented in the written or oral mode. Generally, little difference was found in levels of L2 proficiency between subjects who could correct the error and provide the rule in comparison with those who were only able to correct the error.

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)0.008
metaresearch head score (Gemma)0.063
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.063
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
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 head0.044
GPT teacher head0.290
Teacher spread0.246 · 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

Citations33
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueForeign Language AnnalsSame topicEFL/ESL Teaching and LearningFrench-language works237,207