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Record W4406623840 · doi:10.1080/08941920.2025.2453982

Navigating Cross-Cultural Relationships to Address the Illegal Wildlife Trade: Learning From Western-Interactions With Traditional Chinese Medicine and Traditional Knowledges

2025· article· en· W4406623840 on OpenAlexafffund
David Borish, Alex Basaraba, Michelle Anagnostou

Bibliographic record

VenueSociety & Natural Resources · 2025
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsUniversity of Waterloo
FundersUniversity of WaterlooStanford University
KeywordsWildlifeWildlife tradeGeographyCross-culturalWestern medicineEnvironmental ethicsEcologyEconomic geographyEnvironmental resource managementSociologyTraditional Chinese medicineAnthropologyMedicineBiologyEnvironmental science

Abstract

fetched live from OpenAlex

The illegal wildlife trade is a complex social and ecological issue that requires coordination across different knowledge systems. This perspective paper explores and reframes conservation interventions based on different knowledge systems to address this challenge. Specifically, it highlights the importance of exploring cross-cultural learning between Science and Traditional Knowledges and how they may inform the interactions between Science and Traditional Chinese Medicine in the context of wildlife conservation. Some Western conservationists have urged a reevaluation of TCM due to its departures from Western norms, while concurrently valuing Indigenous TK despite similar divergences. This highlights a notable double standard in the acceptance of different knowledge systems. We emphasize the need to explore how cross-cultural learning between these diverse knowledges can support conservation. The paper concludes with reflections for cross-cultural solutions-building.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score1.000

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 studies0.0020.001
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 head0.068
GPT teacher head0.372
Teacher spread0.303 · 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 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

Citations0
Published2025
Admission routes2
Has abstractyes

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