Social conflict resolution, life history, and the reconstruction of skew
Bibliographic record
Abstract
Models of reproductive skew have provided useful conceptual frameworks for analyzing social conflict and cooperation in groups of reproductively totipotent individuals, in that they specify explicitly how aspects of ecology and genetic relatedness can generate within-group variation in behavior and reproduction. The main outcome of many years of development and application of these models, however, is a growing consensus that transactional models apply to few if any real situations, and that the models cannot be critically evaluated because assembling sufficient quantitative information to allow critical tests of their assumptions and predictions is not feasible. The “top-down” approach of skew modeling, which makes strong yet unsubstantiated assumptions to extract explanations from data, can be contrasted with a “bottom-up” approach, which involves inference of convergences in sets of diverse social, demographic, and life-history traits across highly diverse taxa, and analyses of fine-scale divergences in small sets of traits between conspecific populations and closely related species. The bottom-up approach explicitly recognizes that each population and taxon exhibits a constellation of more or less similar evolutionarily interrelated traits, especially those that affect (1) the ecological circumstances that underly benefits of dividing labor, (2) the opportunities, costs, and benefits of using force (taking control of behavior away), coercion (cost imposition or repression), or persuasion (providing benefits) to modify the expression of conflicts of interest, and (3) the life-history trade-offs and feedbacks that coevolve with social adaptations. I illustrate this approach with examples of social convergences among insects, birds, and mammals (including humans, Homo sapiens ) that yield insights into selective pressures, and present a model based on such convergences for the role of cooperative breeding in the origin and expansion of modern humans.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".