Modeling play: distinguishing between origins and current functions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it