Female assessment: cheap tricks or costly calculations?
Bibliographic record
Abstract
Both commentaries raise the issue of the female capacity to assess complex cognitive traits. We did not focus on this in our review, but we broadly agree with their point: The costs of female assessment are an important constraint on the evolution of complex male displays. Indeed, a key feature of Miller's (2000) “mating mind” hypothesis is that cognition for male displays and cognition for female assessment coevolve, influencing brain structure in both sexes. Whether females can afford to assess complex displays is an important empirical question. Madden et al. (2011) suggest that female bowerbirds have found a way around this problem by using male traits that may be cognitively demanding to produce but are nonetheless cheap to assess. Riebel (2011), surveying recent evidence from songbirds, argues that female preferences involve substantial learning and that costs may exert an influence during development, affecting female judgments of male song later in life.
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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 | 0.019 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.042 | 0.037 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".