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Record W2233058981 · doi:10.18733/c3cc7x

Racializing immigrant professionals in an employment preparation ESL program

2009· article· en· W2233058981 on OpenAlexaffvenueabout
Yan Guo

Bibliographic record

VenueCultural and Pedagogical Inquiry · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsImmigrationFacilitatorEmployabilityEnglish as a second languageGovernmentalityPedagogySociologySociocultural evolutionMulticulturalismEnglish languageDominance (genetics)Gender studiesPolitical sciencePublic relationsPsychologyMathematics educationLaw

Abstract

fetched live from OpenAlex

This article summarizes a case study of the ways in which a specific English as a Second Language (ESL) program prepares immigrant professionals for employment in an urban Canadian labour market. Data for the study were collected from interviews with immigrant professionals, administrators, ESL teachers, a career workshop facilitator, and from classroom observations of the ESL program in an immigrant-serving organization in western Canada. Using the perspectives of critical multiculturalism, critical ultilingualism, and Foucault’s “governmentality,” the study reveals that the ESL program focuses on presentability and employability of immigrants through processes such as acquiring accentless proficiency in English, changing one’s names, and adapting to Canadian linguistic and cultural norms. The ESL program puts the pressure on immigrants to assimilate, without promoting changes in the larger Canadian society. The roots of the dominance of English language and sociocultural norms are not questioned in the program. Finally, major educational implications of these findings 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 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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.315
Threshold uncertainty score0.626

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0430.006
Scholarly communication0.0040.001
Open science0.0020.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.734
GPT teacher head0.666
Teacher spread0.068 · 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 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

Citations32
Published2009
Admission routes3
Has abstractyes

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