Bibliographic record
Abstract
Abstract Play behavior is relatively rare in the animal kingdom, but is widespread, and in some lineages is very common not only in childhood but also in adulthood. It can take many forms, as playful actions can be directed to a social partner (social play), to an inanimate object (object play), or self-directed, as the animal, jumps, runs, and turns (locomotor-rotational play). Considerable progress has been made in understanding the neural, emotional, and cognitive mechanisms mammals use in regulating social play, but whether comparable mechanisms are used to regulate other forms of play, or apply to non-mammalian animals, remains to be resolved. Similarly, social play in some mammals has been demonstrated to benefit the development of sociocognitive skills and emotional resilience, while locomotor-rotational play can benefit the development of motor skills. The factors that allow some species to gain these benefits also remain to be resolved. Statistical approaches that take the relatedness of species into account are increasingly being applied to analyze a growing comparative database that includes species from many different lineages. In addition, mathematical and computational models are being used to test the explanatory power of various factors to account for the evolution of play. Coupled with new methods in neuroscience that provide a deeper understanding of the brain during play, these approaches will enable extraordinary progress in understanding play over the next few decades.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.354 | 0.133 |
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".