Combining data from consumers and traditional medicine practitioners to provide a more complete picture of Chinese bear bile markets
Bibliographic record
Abstract
Abstract Understanding wildlife consumption is essential for the design and evaluation of effective conservation interventions to reduce illegal trade. This requires understanding both the consumers themselves and those who influence their behaviour. For example, in markets for wildlife‐based medicines, both consumers and medical practitioners have a role in which products are consumed. We used mixed methods to triangulate data on bear bile consumption from 3,646 members of the public, 80 pharmacy workers and 38 Traditional Chinese Medicine (TCM) doctors in four provincial capital cities across China. Bear bile can be sold legally in packaged TCM products made from farmed bile, or sold illegally, often as raw gallbladders from wild bears. We interviewed medical practitioners, and surveyed the public using both direct questions (DQ) and the Unmatched Count Technique (UCT), an indirect method used to improve reporting of sensitive behaviours. We applied a ‘combined’ UCT‐DQ analysis to produce a more robust consumption estimate. In all, 140 (3.8%) survey respondents directly reported recent (<3 years) bile consumption, but the combined UCT‐DQ estimate was 11.2%. In total, 14 survey respondents (0.4% sample and 10% recent consumers) self‐reported recent wild bile consumption. Almost a quarter of doctors and half of pharmacy workers had ever prescribed bile. Around half of doctors and over a quarter of pharmacy workers said that bear bile was the best medicine in certain situations. More than half of doctors and over a third of pharmacy workers thought wild bile was more effective than farmed, although we found no evidence of wild bile being formally prescribed. Consumers could name specific treatment uses of bile but almost half of recent consumers did not know the source of bile they had consumed. We show that gathering perspectives from different wildlife market actors can generate a more complete picture of trade. In China, bile consumption may be limited by its specific TCM treatment uses, but whether practitioner views on the greater effectiveness of wild bile are passed to consumers must be investigated further. With potential overlap between farmed and wild consumption, any interventions to change these markets must carefully consider how both consumers and practitioners may react. A free Plain Language Summary can be found within the Supporting Information of this article.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".