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Record W4415357440 · doi:10.1093/biosci/biaf161

Globalizing One Health Requires Consistent and Culturally Appropriate Translation of the Term across Languages

2025· article· en· W4415357440 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBioScience · 2025
Typearticle
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsYork UniversityCentre for Global Health Research
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health Research
KeywordsTerm (time)Articulation (sociology)Culturally appropriateKnowledge translationTranslation (biology)Key (lock)Global health

Abstract

fetched live from OpenAlex

Abstract One Health is a critical approach to global health and its governance. A key challenge in successfully implementing One Health globally is the effective articulation and translation of its ideas, goals, and agenda to align with local contexts, particularly in non-English-speaking regions. In this article, we used document analysis and expert interviews to explore how the term One Health is translated and adopted in Chinese. We identified 20 different Chinese translations of the English term One Health across various document items including policy documents, conference briefs, media news, and journal articles. We examine the causes and implications of these translation inconsistencies and recommend 同一健康 (tongyi jiankang) as the most accurate and culturally appropriate Chinese translation of One Health. Because translation inconsistencies extend beyond Chinese-speaking societies, we discuss how they can pose significant challenges to One Health initiatives more broadly—and ultimately to global health.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.899
Threshold uncertainty score0.113

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.038
GPT teacher head0.373
Teacher spread0.335 · 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