A History of Primatology in Canada and an Introduction to the Special Issue
Bibliographic record
Abstract
Primatological research by anthropologists and evolutionary biologists based in Canada has expanded greatly since its inception ca. 60 years ago. The research foci of the founding primatologists were based on the study of social behaviors to understand human behavior. While Canadian anthropologists have remained interested in how study of our nonhuman primate relatives can inform our understanding of our own species, today the currently active generations of researchers are running labs and research groups focused on a broad range of questions and species and are using an expanded scope of methods to study everything from molecules to metapopulations. We envisioned that this issue of papers would highlight the innovative primate research being conducted by primatologists based in Canada and facilitate further collaboration among researchers, as well as providing a potentially useful introduction for students and postdocs interested in pursuing primatology in Canada. We begin with a historical description of how primatology started and developed in Canada, focusing on three founders of behavioral primatology in Canada - Frances Burton, Linda Fedigan, and Bernard Chapais. We then assess how the next generations have expanded the field significantly. We take a roughly geographical approach, from west to east, in describing the current research programs being done across Canada today and the broad range of topics being investigated. As part of this overview, we also introduce the 18 papers that are part of this special issue.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".