Bibliographic record
Abstract
We present an exploratory study of forest-living orangutan pantomiming, i.e. gesturing in which they act out their meaning, focusing on its occurrence, communicative functions, and complexities. Studies show that captive great apes may elaborate messages if communication fails, and isolated reports suggest that great apes occasionally pantomime. We predicted forest-living orangutans would pantomime spontaneously to communicate, especially to elaborate after communication failures. Mining existing databases on free-ranging rehabilitant orangutans' behaviour identified 18 salient pantomimes. These pantomimes most often functioned as elaborations of failed requests, but also as deceptions and declaratives. Complexities identified include multimodality, re-enactments of past events and several features of language (productivity, compositionality, systematicity). These findings confirm that free-ranging rehabilitant orangutans pantomime and use pantomime to elaborate on their messages. Further, they use pantomime for multiple functions and create complex pantomimes that can express propositionally structured content. Thus, orangutan pantomime serves as a medium for communication, not a particular function. Mining cases of complex great ape communication originally reported in functional terms may then yield more evidence of pantomime.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".