Two faces of environmental effects on mate choice: a comment on Dougherty & Shuker
Bibliographic record
Abstract
Dougherty and Shuker (2015) provide a very useful analysis of the impact of experimental design on estimates of the strength of mate choice preferences. Contrasting paired “no choice” and “choice” designs, their main finding is that choice designs yield estimates of stronger choice. In light of these results, one needs to be careful in the interpretation of choice versus no choice tests. In addition to the specifics of experimental design, in a more general sense, they report evidence for environmental effects (in this case, social environment) on the strength of sexual selection. We see 2 important implications of their findings. First, as the authors note, we should take great care when designing studies of mate choice if our goal is to project our conclusions to natural populations or to make quantitative predictions about how mate choice translates into selection on male traits. If either is our aim, we need to rely on field studies or experimental studies conducted under settings that closely mimic those in the wild. Second, we can gain important insights from studies that focus on how local environmental conditions impact the strength of mate choice or, more generally, sexual selection. Dougherty and Shuker (2015) emphasize the role of mate rejection costs in accounting for the reduced degree of female choice expressed in the no choice tests. In turn, if a role for mate rejection costs is demonstrated, it will yield insights into the economics of mate choice. For example, perhaps the high cost of rejecting a mate in males, where variance in mating success can be so high, accounts for the tendency of male choice to vary little between choice and no choice formats.
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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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.004 | 0.015 |
| Open science | 0.012 | 0.005 |
| Research integrity | 0.041 | 0.063 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".