Having the right face for the job: The effect of facial width‐to‐height ratio on job selection preferences
Bibliographic record
Abstract
Prior research has found that various job candidate characteristics can influence hiring decisions. The current work used experimental methods to test how a novel, appearance-based cue known as a facial width-to-height ratio (fWHR) can bias hiring preferences. A first study provides evidence for our initial hypothesis: people believed high fWHR candidates would be a better fit for blue-collar jobs compared with low fWHR candidates, who were in turn favoured for white-collar jobs. A second study replicates this initial finding and extends it by demonstrating that the effect of fWHR-derived trait inferences of strength and intelligence on hireability predictably varies by job type. Finally, in a third study, we find that this bias reverses when traditional stereotypes of blue-collar and white-collar jobs requiring physicality and intellect are subverted, finding that perceptions of the fit between face type and presumed job requirements matter most for hiring preferences. Together, these findings demonstrate how a seemingly subtle appearance-based cue can have robust implications for hiring.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".