Modeling play: distinguishing between origins and current functions
Bibliographic record
Abstract
Why animals play has been a perennial question, but most of the thinking about this has been framed in terms of its fitness benefits. A review of our present knowledge about the comparative distribution of play suggests that such an approach that leads to claims that the “adaptive value of play is” are misplaced. Play is relatively rare in the Animal Kingdom, indicating that it arose multiple times and that different lineages that have evolved play have transformed it in both divergent and convergent ways. Moreover, some forms of play, especially in its earliest appearance, may have no functional value, with novel functions emerging later as play has been co-opted and transformed for utilitarian purposes. Thus, when it comes to modeling play, care must be taken to differentiate between attempts to explain the origins of play from its current functions, and when current functions are considered, then their variety and likely diverse distribution need be taken into account. Attention to these nuances in the empirical literature, and so developing more targeted models, will provide more focused theoretical developments that can, in turn, stimulate more precise empirical tests. Examples of such models are presented in this issue of the journal.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".