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Record W3041164854

Exploring Opportunities to Modernize Ontario's Approach to Wildlife Health Through Understanding Literature, Stakeholders, Networks, Legislation, and Policy

2020· dissertation· en· W3041164854 on OpenAlexaboutno aff
Diana Sinclair

Bibliographic record

VenueThe Atrium (University of Guelph) · 2020
Typedissertation
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationWildlifeEnvironmental planningPolitical scienceEnvironmental resource managementBusinessPublic administrationGeographyEconomicsEcologyLaw
DOInot available

Abstract

fetched live from OpenAlex

This thesis explores opportunities to modernize how the concept of health is applied to wildlife, using the wildlife health field in Ontario, Canada, as an example. A scoping literature review (458 papers) revealed that wildlife health publications looked at health as the absence of disease (56%, n=257), in an unclear context (37%, n=171), and as a multifactorial entity (7%, n=30). An opportunity exists in the wildlife health literature to consider health and its determinants more broadly, instead of focusing heavily on disease. To understand how different wildlife experts defined wildlife health, eight focus group meetings were used to collect data from stakeholders (biologists, ecologists, veterinarians, wildlife rehabilitators, and hunters and trappers). Thematic analysis demonstrated that there was no single, shared definition of health for wildlife, not even within a stakeholder group. Despite this, all stakeholders saw wildlife health as being multifactorial (i.e., affected by many health determinants). Problems facing wildlife are often highly complex, requiring expertise from many areas. We used ego-centric social network analysis to examine how practitioners shared information between professions and organizations within the Ontario wildlife health network. Analysis of 55 practitioner ego-networks revealed low levels of tie dispersion and high levels of ego-alter similarity. An opportunity exists for wildlife health practitioners to participate in greater sharing of wildlife health information across professional and institutional boundaries. We reviewed legislation and policy guiding Ontario wildlife health (100 documents) to characterize how concepts of health have been applied to wildlife and to assess whether documents enable or prohibit taking a multifactorial health approach. No document defined wildlife health, stated how to measure it, or provided a threshold at which “health” is met. No document outlined bringing information on different health determinants together or sharing wildlife health information between different stakeholders, beyond information on one determinant. An opportunity exists for wildlife health legislation and policy to include context-specific definitions for wildlife health, specify how health is to be measured, and to outline integration of different wildlife health drivers. Doing so will make communicating findings across disciplines and perspectives easier and increase our ability to inform and improve wildlife health management.

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)0.053
metaresearch head score (Gemma)0.054
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.800
Threshold uncertainty score0.928

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0530.054
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.012
Science and technology studies0.0220.025
Scholarly communication0.0220.018
Open science0.0040.016
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.295
GPT teacher head0.298
Teacher spread0.003 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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