The shape of preference functions and what shapes them: a comment on Edward
Bibliographic record
Abstract
Many of us have been baffled by the myriad of terminology in the field of mate choice. Edwards (2015) does us all a favor by clearing up some of this confusion. He points to advantages in being clear about our chosen descriptor of mate choice and he advocates a preference function approach in empirical studies of mate choice. The last point has been championed by many previous contributors to this field and we can only concur: focusing on if and how phenotypic variation in 1 sex relates to reproductive responses in the other captures the gist of sexual selection and avoids many logical pitfalls. However, the relevance of various metrics to some extent depends on the questions asked. For those that are interested in how selection operates within populations, preference functions best define both the source and the shape of sexual selection. Parameters such as mate search effort, mate assessment effort, responsiveness, and discrimination are not so relevant here, but can be of value for those interested in, for example, the economics of mating or condition dependence of mate choice.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".