Trophy hunters pay more to target larger-bodied carnivores
Bibliographic record
Abstract
Hunters often target species that require resource investment disproportionate to associated nutritional rewards. Costly signalling theory provides a potential explanation, proposing that hunters target species that impose high costs (e.g. higher failure and injury risks, lower consumptive returns) because it signals an ability to absorb costly behaviour. If costly signalling is relevant to contemporary 'big game' hunters, we would expect hunters to pay higher prices to hunt taxa with higher perceived costs. Accordingly, we hypothesized that hunt prices would be higher for taxa that are larger-bodied, rarer, carnivorous, or described as dangerous or difficult to hunt. In a dataset on 721 guided hunts for 15 North American large mammals, prices listed online increased with body size in carnivores (from approximately $550 to $1800 USD/day across the observed range). This pattern suggests that elements of costly signals may persist among contemporary non-subsistence hunters. Persistence might simply relate to deception, given that signal honesty and fitness benefits are unlikely in such different conditions compared with ancestral environments in which hunting behaviour evolved. If larger-bodied carnivores are generally more desirable to hunters, then conservation and management strategies should consider not only the ecology of the hunted but also the motivations of hunters.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".