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Record W4408162022 · doi:10.1002/ajp.70019

A History of Primatology in Canada and an Introduction to the Special Issue

2025· article· en· W4408162022 on OpenAlexaffabout
Julie A. Teichroeb, Amanda Melin, Linda M. Fedigan

Bibliographic record

VenueAmerican Journal of Primatology · 2025
Typearticle
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsAlberta Children's HospitalUniversity of CalgaryThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsPrimatologyAnthropologyHistoryGeographySociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.288
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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