Examining discrimination in asynchronous video interviews: Does cultural distance based on country‐of‐origin matter?
Bibliographic record
Abstract
Abstract We conducted two studies to investigate how cultural differences based on country of origin influence the selection process in an asynchronous video interview (AVI) context. We drew upon the GLOBE cultural value dimensions and individual measures of prejudice to examine if raters evaluate job applicants who are more culturally dissimilar to them more negatively than culturally similar applicants. Professionals with hiring experience from the United Kingdom were recruited via the Prolific platform and asked to watch and evaluate pre‐recorded video responses from five culturally diverse applicants. Results across both studies were only somewhat consistent with the GLOBE framework. For instance, raters did demonstrate a strong preference for Canadian and South African interviewees over other countries. Right‐wing authoritarianism and social dominance orientation were non‐significant in moderating how evaluations were assigned; however, ethnocentrism levels did modestly impact evaluations in Study 2. This research is the first to investigate how cultural factors can impact the selection process in an AVI context. As the number of organizations that rely on virtual interviews increases and globalization makes it likely for applicants and interviewers to be from different cultural backgrounds, our research is highly relevant in understanding the impact of these elements on hiring decisions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".