Identifying opportunities toward conflict transformation in an Orca‐Salmon‐Human system
Bibliographic record
Abstract
Abstract Conservation interventions increasingly clash with other human activities, often resulting in conflict among people, communities, and wildlife. One means by which to address and overcome conflicts is through examining their roots in identities and beliefs; in this way, researchers can identify potential routes to conflict interventions that address different kinds—and levels of—conflict often ignored in conventional management. In the Salish Sea region, conflict has emerged following measures by Canada's Department of Fisheries and Oceans that restrict recreational Chinook (Oncorhynchus tshawytscha) fishing to protect endangered Southern Resident Killer Whales (Orcinus ater). Public response has been conflict‐laden, especially between “angler” and “conservation‐supporter” communities—stakeholder groups portrayed in the media as distinct and opposed. We used online surveys to examine the identity, beliefs, and opinions of stakeholders. Most survey participants (n = 727) self‐identified saliently as either conservation‐supporters (53%) or anglers (34%), although some held both identities. Both groups scored similarly high in environmental and stakeholder identity affiliation scores, also showing association between the intensity of identities with public engagement in management discourse. Groups differed strongly (χ2 = 156.27, p <.001) in management beliefs, with conservation supporters favoring core management priorities of species conservation, while anglers favored a balanced or natural resource‐oriented approach. Despite divergences in beliefs and management priorities, more individuals self‐identified as both anglers and conservation‐supporters than one would expect based only on existing media portrayals. Ultimately, our results identify conflicts between stakeholder groups as deeply‐embedded. Commonalities (in identities and beliefs regarding Chinook), however, suggest a path forward that draws on conservation conflict transformation theory. Broadly, our approach offers new generalizable insight into the levels‐of‐conflict framework to inform scholarly and practical endeavors.
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 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 | 0.002 | 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 | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".