MétaCan
Menu
Back to cohort
Record W4386137933 · doi:10.1017/one.2023.2

A reimagined One Health framework for wildlife conservation

2023· article· en· W4386137933 on OpenAlexafffundabout
Craig Stephen, Alana Wilcox, Sarah Sine, Jennifer F. Provencher

Bibliographic record

VenueResearch Directions One Health · 2023
Typearticle
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsEnvironment and Climate Change Canada
FundersEnvironment and Climate Change Canada
KeywordsWildlifeFraming (construction)Health equityEquity (law)GeographyOne HealthPublic relationsEnvironmental planningEnvironmental resource managementBusinessPsychologyPolitical scienceSociologyEcologyPublic healthMedicineNursingBiologyEconomics

Abstract

fetched live from OpenAlex

Abstract The One Health discourse is dominated by the role of animal health as a determinant of human health. This discourse often disregards the intrinsic and ecological value of healthy animals and is thus an inadequate framing for wildlife conservation. Our paper reimagines One Health for conservation purposes based on five premises: (i) health is cumulative; (ii) there are multiple species with different health needs and goals in the same setting; (iii) One Health emphasizes “bundled” relationships unique to a setting, rather than independent and intersecting spheres of health; and One Health should be (iv) equity informed and (v) have a shared goal that can be achieved through intersectoral actions. The reimagined framework is centered on the guiding principle that all actions should ensure no species or generation is prevented from reaching good health by the actions to protect other species or generations. Grounded in the positive outcome of health equity, the framework uses three prompts to guide One Health planning – populations, places and goals. The paper discusses how the framework can be applied for research concerning wood bison herds under imminent threat in Canada.

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)0.008
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.800
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.343
GPT teacher head0.528
Teacher spread0.186 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations21
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueResearch Directions One HealthSame topicZoonotic diseases and public healthFrench-language works237,207