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Record W2150184189 · doi:10.1017/s0261444809990073

Research in the Modern Language Centre at the Ontario Institute for Studies in Education of the University of Toronto (OISE/UT)

2009· article· en· W2150184189 on OpenAlexaffabout
Younhee Kim, Robert Kohls, Christian W. Chun

Bibliographic record

VenueLanguage Teaching · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversity of Toronto
FundersUniversité de GenèveUniversiteit van Amsterdam
KeywordsPedagogyMultilingualismLiteracyCurriculumSociologyApplied linguisticsForeign languageLanguage educationDiversity (politics)Language industryLanguage policyPolitical scienceLinguisticsComprehension approach

Abstract

fetched live from OpenAlex

The Modern Language Centre addresses a broad spectrum of theoretical and practical issues related to second and minority language teaching and learning. Since its foundation in 1968, the quality and range of the Centre's graduate studies programs, research, and development projects and field and dissemination services have brought it both national and international recognition. Our work focuses on curriculum, instruction, and policies for education in second, foreign, and minority languages, particularly in reference to English and French in Canada but also other languages and settings – including studies of language learning, methodology and organization of classroom instruction, language education policies, student and program evaluation, teacher development, as well as issues related to bilingualism, multilingualism, cultural diversity, and literacy. In this research report, we will present research activities underway in the Centre in the areas of pedagogy, literacy development, sociocultural theory, pragmatics, and assessment.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.921
Threshold uncertainty score0.429

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0110.004
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0210.001

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 head0.074
GPT teacher head0.348
Teacher spread0.275 · 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 designNot applicable
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

Citations3
Published2009
Admission routes2
Has abstractyes

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