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Record W1657307002 · doi:10.18806/tesl.v23i1.75

Developing Cognitive Academic Language Proficiency: The Journey

2005· article· en· W1657307002 on OpenAlexfundvenueno aff
Hetty Roessingh, Pat Kover, David Watt

Bibliographic record

VenueTESL Canada Journal · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
FundersBiodesign Institute, Arizona State UniversityUniversity of CambridgeBộ Giáo dục và Ðào tạoUniversity of Calgary
KeywordsPsychologyLanguage proficiencyCognitionCompetence (human resources)Second-language acquisitionLanguage acquisitionHigher educationPedagogyMathematics educationDevelopmental psychologyLinguisticsSocial psychology

Abstract

fetched live from OpenAlex

This study tracks the development of cognitive academic language proficiency of 47 academically competent high school ESL learners of differing age on arrival (AOA) who received instructed ESL support and one comparison group of six young arrivals who received little if any ESL support during their educational experiences. Although intake and outcome measures appear similar on the surface for all 47 students, variability in the subscores of the outcomes measure provided the catalyst for taking a closer look at progress during the ESL program. The outcomes provide a refined understanding of the development of cognitive academic language proficiency, and in particular the role of underlying proficiency and structured ESL support. The data suggest that the youngest arrivals (i.e., those aged 6-11) remain at risk in their postsecondary education. The outcomes also suggest that the acquisition of cultural capital and metaphoric competence remains a challenge for all learners.

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)0.006
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.101
GPT teacher head0.480
Teacher spread0.379 · 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

Citations35
Published2005
Admission routes2
Has abstractyes

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Same venueTESL Canada JournalSame topicMultilingual Education and PolicyFrench-language works237,207