Play fighting revisited: its design features and how they shape our understanding of its mechanisms and functions
Bibliographic record
Abstract
Play fighting has been one of the most intensely studied forms of play and so has provided some of our deepest insights into the understanding of play in general. As the label implies, this behavior resembles serious fighting, in that the animals compete for an advantage over one another, but unlike true aggression, for play fighting to remain playful, it also incorporates a degree of cooperation and reciprocity – restrained competition seems to be its hallmark. Despite these common features, it should be noted that both the advantage competed over and the mechanisms by which restraint is achieved varies across species. Such variation mitigates simple generalities. For example, how empirical support for a proposed adaptive function in one species not being replicated in another, is to be interpreted. What has emerged over the past few decades is that play fighting is diverse, varying across several dimensions, some superficial, some fundamental, making choosing species to compare a challenge. In this paper, we explore various design features that constitute play fighting and the ways these can be modified across different species and lineages of species. Given that a major pillar of ethology is that description precedes explanation, having a good grasp of the behavioral diversity of play fighting is an essential starting point for detailed analyses of the mechanisms and functions of play. We show that commonalities across species likely involve different mechanisms than do species idiosyncrasies, and that different styles of play fighting likely afford different adaptive opportunities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".