The elephant (head) in the room: A critical look at trophy hunting
Bibliographic record
Abstract
Abstract Trophy hunting has occupied a prominent position in recent scholarly literature and popular media. In the scientific conservation literature, researchers are generally supportive of or sympathetic to its usage as a source of monetary support for conservation. Although authors at times acknowledge that trophy hunting faces strong opposition from many members of the public, often for unspecified reasons associated with ethics, neither the nature nor the implications of these ethical concerns have been substantively addressed. We identify the central act of wildlife “trophy” taking as a potential source of ethical discomfort and public opposition. We highlight that trophy hunting entails a hunter paying a fee to kill an animal and claim its body or body parts as a trophy of conquest. Situating this practice in a Western cultural narrative of chauvinism, colonialism, and anthropocentrism, we argue trophy hunting is morally inappropriate. We suggest alternative strategies for conservation and community development should be explored and decisively ruled out as viable sources of support before the conservation community endorses trophy hunting. If wildlife conservation is broadly and inescapably dependent on the institution of trophy hunting, conservationists should accept the practice only with a due appreciation of tragedy, and proper remorse.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.029 | 0.039 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".