Large carnivore hunting and the social license to hunt
Bibliographic record
Abstract
The social license to operate framework considers how society grants or withholds informal permission for resource extractors to exploit publicly owned resources. We developed a modified model, which we refer to as the social license to hunt (SLH). In it we similarly consider hunters as operators, given that wildlife are legally considered public resources in North America and Europe. We applied the SLH model to examine the controversial hunting of large carnivores, which are frequently killed for trophies. Killing for trophies is widespread, but undertaken by a minority of hunters, and can pose threats to the SLH for trophy-seeking carnivore hunters and potentially beyond. Societal opposition to large carnivore hunting relates not only to conservation concerns but also to misalignment between killing for trophies and dominant public values and attitudes concerning the treatment of animals. We summarized cases related to the killing of grizzly bears (Ursus arctos), wolves (Canis lupus), and other large carnivores in Canada, the United States, and Europe to illustrate how opposition to large carnivore hunting, now expressed primarily on social media, can exert rapid and significant pressure on policy makers and politicians. Evidence of the potential for transformative change to wildlife management and conservation includes proposed and realized changes to legislation, business practice, and wildlife policy, including the banning of some large carnivore hunts. Given that policy is ultimately shaped by societal values and attitudes, research gaps include developing increased insight into public support of various hunting policies beyond that derived from monitoring of social media and public polling. Informed by increased evidence, the SLH model can provide a conceptual foundation for predicting the likelihood of transient versus enduring changes to wildlife conservation policy and practice for a wide variety of taxa and contexts.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".