Following the human point: Research with nonhuman animals since Povinelli, Nelson, and Boysen (1990)
Bibliographic record
Abstract
For this special issue in honor of Dr. Sarah (Sally) Boysen’s career, we review studies on point following in nonhuman animals. Of the 125 papers that we documented on this topic published since the publication of Povinelli, Nelson, and Boysen (1990), 93 (74%) were published in the past fifteen years, including 21 in the past five years, indicating that this topic is still an active area of interest in the field of animal behavior and cognition. We present results of a survey of publication trends, discussing the species tested and the sample sizes, and we note methodological considerations and current multi-laboratory approaches. We then categorize and synthesize the research questions addressed in these studies, which have been at both the ultimate level (e.g., questions related to evolutionary adaptiveness and phylogenetic differences) and proximate level (e.g., questions related to experiential and temperamental processes). Throughout, we consider future directions for this area of research.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".