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Record W4399256257 · doi:10.1080/10871209.2024.2360741

The social landscape of wolves in Canada - preliminary findings

2024· article· en· W4399256257 on OpenAlexaffabout
Stephanie Rutherford, Valli-Laurente Fraser-Celin, Heather A. Hager, Laurie Fox, Anton H. Hofman

Bibliographic record

VenueHuman Dimensions of Wildlife · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsWilfrid Laurier UniversityUniversity of GuelphTrent University
Fundersnot available
KeywordsGeographyWildlifeEcologyEnvironmental resource managementBiologyEnvironmental science

Abstract

fetched live from OpenAlex

Despite the long-standing presence of wolves (Canis lupus) in Canada, attitudes toward wolves have been understudied at the national scale. However, such data can inform wildlife management policies and robust coexistence strategies. We developed an 11-question survey to assess “the social landscape” of wolves in Canada. We explored the following research questions: 1) Are attitudes toward wolves generally positive? 2) What is the role of proximity on attitudes toward and tolerance for wolves? 3) What demographic characteristics matter most? Preliminary results indicate that Canadians have positive attitudes toward wolves regardless of age, gender, province, and vote in the last federal election; only ethnicity had a statistically significant effect. We also found that positive attitude is related to proximity and tolerance. More research with larger sample sizes and more focused surveys are needed to delve deeper into these preliminary results.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2024
Admission routes2
Has abstractyes

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