Are human faces and voices ornaments signaling common underlying cues to mate value?
Bibliographic record
Abstract
Abstract In our daily lives, we constantly interact with people. We maintain relationships with families and friends. We collaborate with colleagues. We seek passion with our lovers and avoid conflicts with our enemies. How we divide the world into these and many other categories of people is initially guided by our first impressions of how they look and sound. Many times we are surprised when we hear someone on the phone whom we have not yet met face‐to face; they sound different from what we imagined. There are, however, many things that we are not surprised about in such situations. People are accurate at identifying sex, health, emotions, and age by both voices and faces.3–12 There is good evidence that many seemingly disparate ornaments such as body and face,13 body and voice,14–16 and face and odor19 may convey either backup signals of the same underlying quality20,21 or convey signals of different underlying qualities that are used in conjunction to provide a more robust view of the organism's overall fitness.22,23 Is this also true of face and voices? Until recently, little attention has been given to the idea that people's faces and voices might both signal the same underlying qualities related to hormone levels, and that we might use these hormonal fitness markers to provide a better picture of the signaler's overall mate value.20,21,24 In this paper I first argue that aspects of voices and faces can be used as markers of hormonal status. Second, I argue that both vocal and facial features associated with hormonal status are used by people to assess mate quality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".