On male harm: How it is measured and how it evolves in different environments
Bibliographic record
Abstract
Males can harm the females they interact with, but populations/species vary widely in the occurrence and extent of harm. We consider the merits and limitations of two common approaches to investigating male harm and apply these to an experimental study of divergence in harm. Different physical environments can affect how the sexes interact, causing plastic and/or evolved changes in harm. If harmful male phenotypes are less likely to evolve in situations where females have more control over sexual interactions, populations evolving in environments in which females have greater control should have less harmful males. We test this idea using experimental populations of Drosophila melanogaster that have evolved in either of two environments that vary in the extent to which females can avoid males, or in a third environment without mate competition (i.e., enforced monogamy). We demonstrate an evolved reduction in harm in the absence of mate competition and also in a mate competition environment in which females have greater control. We also show a plastic effect in that otherwise harmful males are no longer so when tested in the environment in which females have greater control. Our results reveal the different perspectives provided by the two methods of studying harm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.023 |
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".