Bibliographic record
Abstract
This book arose from three realizations. First, there is an important need for good models of great ape cognitive evolution. Studies of comparative primate cognition over the last two decades increasingly show that all great apes share a grade of cognition distinct from that of other nonhuman primates. Their cognition appears to be intermediate in complexity between that of other nonhuman primates and humans, so it offers the best available model of the cognitive platform from which human cognition evolved. Understanding the position of the great apes is then essential to understanding cognitive evolution within the primate order and ultimately, in humans. Second, existing reconstructions of the evolutionary origins of great ape cognition are all in need of revision because of advances in research on great ape cognition itself, on modern great ape adaptation, and on fossil hominoids. Third, developing an accurate picture of the evolutionary origins of great ape intelligence requires bringing together expertise from a highly diverse range of fields beyond modern great ape cognition. Essential are current understandings of the brain, life histories, social and ecological challenges, and the interactions among them in both living and ancestral hominids. We therefore assembled a team of contributors with expertise spanning the topics currently recognized as relevant to cognitive evolution in the great ape lineage, with the aim of piecing together the most comprehensive picture possible today. We asked all our contributors to explore the implications of their realm of expertise for cognition and cognitive evolution.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.402 | 0.226 |
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".