Bibliographic record
Abstract
We review the causes of the evolution of social systems and the methods used in their analysis. First, we discuss the roles of genetics, phenotypic traits, ecology (basic necessary resources and natural enemies) and demography in the origin and evolution of sociality, and synthesize the effects of these conditions in a comparative assessment of the predictions of optimal skew models. The models provide a useful framework to explaining and predicting social systems, but would benefit from expansion in the range of their assumptions and more explicit connection to ecological and demographic selective pressures. Second, we review the purposes and usefulness of alternative social system lexicons. We conclude that the trade–off between universality and taxon–specific precision of terms can usefully be addressed by explanation of social terms for each comparative test coupled with striving for recognition of convergence across the broadest possible taxonomic range. Finally, we provide an overview of current adaptationist methods used for analyzing social systems, focussing on approaches that utilize phylogenetic information. Integration of comparative with behavioral–ecological methods, especially experimentation, promises to lead to the next series of insights and critical data for tests of theory. INTRODUCTION This volume has had three main objectives. First, we have tried to bring together the widest possible diversity of social insects and arachnids, to elucidate necessary and sufficient conditions for the origin and maintenance of different social systems. In this chapter, we first review the evidence for associations between social systems and genetic, phenotypic, ecological and demographic variables.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".