Conscientiousness cues in AVIs : how cues interact
Bibliographic record
Abstract
The rise of virtual interviewing technology, notably Asynchronous Video Interviews (AVIs), has transformed personnel selection practices worldwide due to their cost and time efficiencies.Yet, research on potential biases in AVIs, particularly concerning contradictory cues impacting perceived applicant personality, remains scarce.I conducted a 2x2x2 design (messiness) x (professional dress) x (job type) to examine the possible buffering effect messiness has on the perception of professional dress, the heightened importance of conscientiousness-related cues when selecting canidates for certain jobs and these conscientiousness-related cues's biasing effects on perceived conscientiousness and final interview outcomes.Results reveal environmental cleanliness significantly affects perceived conscientiousness and hireability, with tidier settings favoring candidates.Additionally, technical role applicants are perceived as more conscientious than those in client-facing positions.Notably, candidates in client-facing roles with formal attire and messy backgrounds received lower scores, emphasizing the importance of recording in tidy environments or utilizing background filters for fairness in hiring processes.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".