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Record W4292323916 · doi:10.1177/01461672221115943

High-Quality Contact With Fellow Majority Group Students Is Associated With Better Academic Performance of Minority Group Students

2022· article· en· W4292323916 on OpenAlexaff
Rotem Kahalon, Nurit Shnabel, Keren Sharvit, Samer Halabi, Stephen C. Wright

Bibliographic record

VenuePersonality and Social Psychology Bulletin · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPsychologyContext (archaeology)Interpersonal communicationJudaismSocial psychologyContact theoryInterpersonal relationshipContact hypothesisQuality (philosophy)Academic achievementMathematics educationTheology

Abstract

fetched live from OpenAlex

We examined the association between intergroup contact and academic performance at university among minority students in a context with a segregated pre-university school system. Study 1 tested whether participation in a group dynamics course, which involves intimate interpersonal contact between Israeli Arab ( n = 125) and Jewish students, was associated with better grade point average (GPA). As expected, Arab students who participated in the course had a higher GPA than those who did not, even when controlling for pre-university achievements. The corresponding difference among Jews was substantially smaller. Study 2 ( N = 90), a longitudinal study, revealed that the quality of contact with Jewish students at university was associated with Arab students’ subsequent higher GPA, even when controlling for pre-university contact, proxies of academic achievements, and perceptions of intergroup relations. The quality of contact with Jewish students was also associated with Arab students’ sense of academic belonging. Theoretical and practical implications 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)0.001
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.038
GPT teacher head0.366
Teacher spread0.328 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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