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Record W2944554183 · doi:10.47678/cjhe.v16i3.188405

Academic Perceptions of Immigrant and Minority Postsecondary Students

2018· article· en· W2944554183 on OpenAlexaffvenueabout
Bette DeBellefeuille, Philip C. Abrami

Bibliographic record

VenueCanadian Journal of Higher Education · 2018
Typearticle
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsConcordia University
Fundersnot available
KeywordsLuckPsychologyImmigrationPerceptionSocial psychologyTask (project management)Academic achievementHigher educationAttributionDevelopmental psychology

Abstract

fetched live from OpenAlex

This investigation compared postsecondary students whose mother tongue was English and who were natives of Canada (TV = 117) with minority students (N = 91) in terms of: a) the perceived importance of a university education and the perceived likelihood of academic success; b) the estimated likelihood of success at both competitive and noncompetitive tasks; c) the causal attributions for task outcomes and affective reactions to those outcomes and d) one projective and fourobjective fear of success (FOS) measures. English Canadian students and minority students held equivalent views on the importance of a university education and a successful career. FOS scores did not differ between the groups regardless of the measure used, either for males or females. Although there were few differences between the groups in their reaction to competitive, achievement-oriented tasks, there were more differences between the groups in their reactions to noncompetitive tasks. Here, minority students expressed some negative affective reactions. The minority students believed that external factors, particularly luck, had a greater influence on task outcome than did English Canadians.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.374
Teacher spread0.354 · 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 designObservational
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

Citations0
Published2018
Admission routes3
Has abstractyes

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