Naturalistic Approaches to Orangutan Intelligence and the Question of Enculturation
Bibliographic record
Abstract
Field studies have been, and continue to be, important contributors to the understanding of great ape cognition-especially with regard to questions of cognitiveecology or the key cognitive challenges in the evolution of primate intelligence. Theyare also critical to resolving a current debate, whether human enculturation boosts great apes' cognition, because only studies of problem-solving in feral contexts can resolve the question of whether abilities are higher in enculturated than non-enculturated great apes. To this debate, this paper offers findings from observational field studies on freeranging rehabilitant orangutans' cognitive capabilities, as revealed in their food processing and arboreal positioning, and on the possible social transmission of that expertise. These findings are combined with published findings on wild and enculturated great apes as a basis for assessing the effects of human enculturation on great ape cognition. This assessment joins several others in showing that free-ranging great apes independently achieve cognition of the same order of complexity as enculturated great apes, in concluding that claims for the effects of human enculturation are likely inflated, and in suggesting that the basis for the effectiveness of human enculturation is that great apes normally "enculturate" themselves.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.018 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".