Scene, not heard: Exploring the influence of socioeconomic status background cues in asynchronous video interviews
Bibliographic record
Abstract
Abstract While recent studies have highlighted the potential for background cues in asynchronous video interviews (AVIs) to inadvertently disclose non‐job‐related information about job applicants, researchers have yet to explore the impact of socioeconomic status (SES) cues. This study investigates whether background cues about SES (specifically cues not available in a face‐to‐face interview) introduce unique biases in the hiring process. We examined if evaluators could discern SES differences based on a job applicant's background and whether these cues influenced the perceived hireability of the job applicant. To enhance the realism of our findings and understand when such biases may be exacerbated, we simulated the conditions a hiring manager might face by inducing cognitive load (CL). In a working sample of N = 260 American Cloud Research Connect participants, we used a 2 (low; high SES) by 2 (low; high CL) between‐subjects experimental design. We found that although evaluators could identify differences in SES and did experience a difference in CL, these two factors did not directly influence the perceived hireability of the job applicant. We also investigated the role of evaluators' characteristics, such as their own SES, attitude towards poverty, and social dominance orientation. Although these did not directly influence their ratings of the job applicant, we identified noteworthy correlations: participants' perceptions of the SES of the background correlated with the job applicant's (a) perceived hireability, and (b) perceived SES. These findings emphasize the need for further research into the subtle cues that evaluators might use to gauge SES, which could impact a job applicant's AVI evaluation.
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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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".