The pleasure of pursuit: recreational hunters in rural Southwest China exhibit low exit rates in response to declining catch
Bibliographic record
Abstract
Hunting is one of the greatest threats to tropical vertebrates. Examining why people hunt is crucial to identifying policy levers to prevent excessive hunting. Overhunting is particularly relevant in Southeast Asia, where a high proportion of mammals and birds are globally threatened. We interviewed hunters in Southwest China to examine their social behavior, motivations, and responses to changes in wildlife abundance. Respondents viewed hunting as a form of recreation, not as an economic livelihood, and reported that they would not stop hunting in response to marked declines in expected catch. Even in scenarios where the expected catch was limited to minimal quantities of small, low-price songbirds, up to 36.7% of respondents said they would still continue to hunt. Recreational hunting may be a prominent driver for continued hunting in increasingly defaunated landscapes; this motivation for hunting and its implications for the ecological consequences of hunting have been understudied relative to subsistence and profit hunting. The combination of a preference for larger over smaller game, reluctance to quit hunting, and weak enforcement of laws may lead to hunting-down-the-web outcomes in Southwest China.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".