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Record W2030789358 · doi:10.3138/cmlr.66.5.677

Toward Understanding the Academic Trajectories of ESL Youth

2010· article· en· W2030789358 on OpenAlexvenueaboutno aff
Bruce Garnett

Bibliographic record

VenueCanadian Modern Language Review/ La Revue canadienne des langues vivantes · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsGraduation (instrument)Christian ministryCohortMultivariate statisticsAffect (linguistics)English languagePsychologyBaseline (sea)Multivariate analysisAcademic achievementMathematics educationDemographyGeographySociologyPolitical scienceStatisticsMathematics

Abstract

fetched live from OpenAlex

This study explores the variations in the academic trajectories of socio-demographic subgroups of English as a second language (ESL) students (N = 7527) in British Columbia, Canada. Results are compared to a native English speaker baseline (N = 37,612). Longitudinal data describing the 1997 Grade 8 cohort (i.e., students age 13, typically in their first year of secondary school) were obtained from the British Columbia Ministry of Education. Students are disaggregated by English proficiency, language spoken at home, and socio-economic status to indicate, through cross-tabulations and multivariate regression models, the effects of these variables on graduation and on enrolment and performance in senior-level English and mathematics courses associated with university entrance. Results are interpreted through a framework adapted from Cummins (1997). Ethnocultural background, as proxied by language spoken at home, predicts trajectories robustly; an indicator of socio-economic status only partially attenuates its effects. Background factors such as English proficiency affect different ethnocultural groups differently. The variation under the ESL label and the need to disaggregate data for decision-making purposes are discussed.

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 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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.100
GPT teacher head0.367
Teacher spread0.267 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations22
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Modern Language Review/ La Revue canadienne des langues vivantesSame topicMultilingual Education and PolicyFrench-language works237,207