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ARE HIGHLY STRUCTURED JOB INTERVIEWS RESISTANT TO DEMOGRAPHIC SIMILARITY EFFECTS?

2010· article· en· W2114293769 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePersonnel Psychology · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologySimilarity (geometry)InterviewRace (biology)Social psychologySample (material)Personnel selectionValue (mathematics)Selection (genetic algorithm)Job performanceJob satisfactionManagementStatisticsSociologyGender studies

Abstract

fetched live from OpenAlex

This study examines the extent to which highly structured job interviews are resistant to demographic similarity effects. The sample comprised nearly 20,000 applicants for a managerial‐level position in a large organization. Findings were unequivocal: Main effects of applicant gender and race were not associated with interviewers’ ratings of applicant performance nor was applicant–interviewer similarity with regard to gender and race. These findings address past inconsistencies in research on demographic similarity effects in employment interviews and demonstrate the value of using highly structured interviews to minimize the potential influence of applicant demographic characteristics on selection decisions.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.313
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.089
GPT teacher head0.356
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