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Record W2099185965 · doi:10.1080/10871209.2015.1046095

Stakeholder Perspectives on Chronic Wasting Disease Risk and Management on the Canadian Prairies

2015· article· en· W2099185965 on OpenAlexaffabout
Kari Amick, Douglas A. Clark, Ryan K. Brook

Bibliographic record

VenueHuman Dimensions of Wildlife · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicQ Methodology Applications
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsChronic wasting diseaseWastingStakeholderEnvironmental planningGeographyWildlife managementEnvironmental resource managementRisk managementDiseaseEnvironmental healthBusinessSocioeconomicsWildlifeMedicinePolitical scienceEcologyPublic relationsSociologyBiologyEnvironmental science

Abstract

fetched live from OpenAlex

Chronic wasting disease (CWD) is an infectious disease caused by a prion that results in neurodegeneration and death in cervids. This study uses Q methodology to characterize stakeholder perspectives about CWD risk and management on the Canadian prairies, and to understand the potential for CWD management using an adaptive governance framework. Workshops and individual interviews were conducted with 16 stakeholders in Saskatchewan and Manitoba. Problem definitions framed CWD as a technical problem calling for technical solutions. All perspectives on solutions focused on the importance of education and the idea that management should fit within a national management strategy. A unique Aboriginal perspective also emerged and warrants further exploration. Results also indicated that although stakeholders wish to be involved with CWD management, they trust and expect government leadership, and are disinterested in adaptive governance. Challenges for stakeholder involvement in Canadian CWD management include a lack of sufficient leadership and general ambivalence.

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)0.010
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.247

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0180.007
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.354
GPT teacher head0.410
Teacher spread0.056 · 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 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

Citations13
Published2015
Admission routes2
Has abstractyes

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