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Record W2607335649 · doi:10.1177/0033688217698294

Taking Stock of Corpus-Based Instruction in Teaching English as an International Language

2017· article· en· W2607335649 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.

Bibliographic record

VenueRELC Journal · 2017
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsLinguisticsCorpus linguisticsLanguage educationLanguage assessmentTeaching methodComputer sciencePsychologyMathematics education

Abstract

fetched live from OpenAlex

Corpora are essential tools in the teaching of English as an international language (EIL). With the advent of high-powered computers, online corpora have been developed with the potential to transform how EIL is taught both inside and outside the classroom, since anyone with a mobile device and internet access can now take advantage of numerous corpora databases. But applying computer corpora to language pedagogy also requires teacher mediation; moreover, the issues involving the lack of corpus integration in either the EIL language classroom or teacher training programmes are both challenging and complex. Nonetheless, there is hope that empowering teachers with the necessary tools, skills, and knowledge in using online corpora will lead to the day when corpora resources and their use are no longer the exclusive preserve of researchers and reference material developers.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.841
Threshold uncertainty score0.981

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0200.000

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 head0.023
GPT teacher head0.380
Teacher spread0.357 · 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