The sociality–health–fitness nexus: synthesis, conclusions and future directions
Bibliographic record
Abstract
This theme issue has highlighted the links between sociality, health and fitness in a broad range of organisms, and with approaches that include field and captive studies of animals, comparative and meta-analyses, theoretical modelling and clinical and psychological studies of humans. In this concluding chapter, we synthesize the results of these diverse studies into some of the key concepts discussed in this issue, focusing on risks of infectious disease through social contact, the effects of competition in groups on susceptibility to disease, and the integration of sociality into research on life-history trade-offs. Interestingly, the studies in this issue both support pre-existing hypotheses, and in other ways challenge those hypotheses. We focus on unexpected results, including a lack of association between ectoparasites and fitness and weak results from a meta-analysis of the links between dominance rank and immune function, and place these results in a broader context. We also review relevant topics that were not covered fully in this theme issue, including self-medication and sickness behaviours, society-level defences against infectious disease, sexual selection, evolutionary medicine, implications for conservation biology and selective pressures on parasite traits. We conclude by identifying general open questions to stimulate and guide future research on the links between sociality, health and fitness.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".