Bibliographic record
Abstract
Our perception of others influence who we decide to approach or avoid, even from minimal information (e.g., a person’s facial appearance). Pertinently, individuals who are perceived as more leaderlike (i.e., dominant, competent) are more likely to be elected into office or even hired as CEO’s (Rule & Ambady, 2009). In the present study, we aim to investigate whether agentic (e.g., dominance, competence) or communal traits (e.g., warmth, trustworthiness) are favored among high-status women. In the present study, we will recruit 200 MacEwan University students via SONA (online research database) to complete an online questionnaire. Participants will view 200 facial photographs of female surgeons from across Canada. Participants will indicate their rating of each individual on one trait (e.g., dominance, competence, surgeon-like). In addition, the participants will be in either one of two conditions: they will be told that they are rating female surgeons or not. I expect to find that female faces perceived as more agentic (vs. communal) will also be perceived as more surgeon-like or leader-like. This effect should be amplified when participants know they are rating female surgeons. If these findings hold true, it suggests that facial perception plays important roles in determining one’s status, specifically for females in male-dominated roles. Faculty Mentor: Miranda Giacomin Department: Psychology
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".