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Record W4225298430 · doi:10.1016/j.onehlt.2022.100393

Context matters: Leveraging anthropology within one health

2022· article· en· W4225298430 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOne Health · 2022
Typearticle
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsContext (archaeology)WildlifeOne HealthEnvironmental ethicsEthnographyEndangered speciesSociologyValue (mathematics)Psychological interventionEcologyGeographyPsychologyPublic healthAnthropologyComputer scienceMedicineBiology

Abstract

fetched live from OpenAlex

Anthropologists develop long-term engagements with communities, animals, and the ecosystems they all share. This approach can provide important context that is necessary for One Health research, which may otherwise overlook the perspectives and lived experiences of community members. This paper presents two case studies that illustrate the importance of leveraging long-term, holistic, engagements with communities in moving the One Health concept forward. The first illustrates the complexity of understanding the health of people and animals within the context of environmental change in South India. The second provides insights into how the conservation of endangered species requires considering the entanglements of people, domestic animals, and the landscapes they share with wildlife in Madagascar. We demonstrate the value of integrating anthropological perspectives within interdisciplinary One Health research and interventions to better understand the complexity of systems.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.658
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.073
GPT teacher head0.355
Teacher spread0.282 · 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