Animal welfare, social license, and wildlife use industries
Bibliographic record
Abstract
ABSTRACT Many wildlife use industries are facing criticism from animal welfare groups. In some recent cases, opposition to contentious practices (e.g., kangaroo [Macropus spp.] harvesting) has achieved widespread community support and industries have lost market access or regulatory approval. The concept of social license to operate has become an important focus for many natural resource management fields, but there is ostensibly less awareness of its role in animal industries. To regard this contemporary threat to traditional wildlife management as more than inexplicable requires some delving into social sciences. We use the example of the declining harp seal (Pagophilus groenlandicus) harvest in Canada to illustrate how poorly addressed animal welfare concerns can erode social license and decimate even ecologically sustainable wildlife use enterprises. We argue that other consumptive wildlife use industries, such as North American fur harvesting and kangaroo harvesting in Australia are at risk of loss of social license if animal welfare concerns are not addressed proactively and effectively. When faced with opposition from animal advocacy groups, many wildlife use industries have traditionally been reactive and have been reluctant to engage with stakeholders who possess seemingly irreconcilable differences. Instead, industries have often resorted to secrecy or deception, or have steadfastly defended their current approaches while attacking their critics. We suggest that a more effective approach would be for industries to proactively engage with stakeholders, establish a shared vision for how their industry should operate, and support this vision by transparently monitoring animal welfare outcomes. Proactive management of community expectations surrounding animal welfare is essential for the maintenance of social license for wildlife use enterprises. © 2018 The Wildlife Society.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".