On Animates and Other Worldly Things
Bibliographic record
Abstract
Jean Mandler has made fundamental contributions to our understanding of the origin and development of concepts. These include her elegant theoretical and experimental work on scripts and schemas, memory, representation and infant cognition. Her theoretical papers about “how to make a baby ” are classics. Mandler started her developmental work relatively late in her scientific career, having focussed initially on animal learning. I had the good fortune to be an undergraduate at the University of Toronto, where Jean Mandler had an animal lab. So, I learned first hand that, from the beginning, she was pondering the roles of attention and concepts. This was not exactly the “in ” thing to do at the time. Behaviorism reigned and talk about mental matters was viewed as unscientific by almost everyone. No matter, Jean always was open to idea that non- or pre-linguistic individuals might be able to think. Amazingly, a considerable number of students of early cognitive development still reject this possibility. Journals and meetings are full of efforts to explain away the converging lines of evidence. This is especially puzzling given that Jean did not start out with the idea that infants use general categories. Instead, she moved towards it because
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".