Trust, Control, and Risk in the Salish Sea: A Case Study of the Transboundary Network Governing the Endangered Southern Resident Killer Whale
Bibliographic record
Abstract
The Salish Sea is the inland body of water shared between Vancouver Island and the mainland of British Columbia and Washington. It is home to more than 100 endangered species, including the southern resident killer whale (SRKW, Orcinus orca). SRKWs first received the designation of endangered in 2003 in Canada, followed by the 2005 listing in the United States (U.S.). Despite their endangered status, the population of SRKWs continues to decline. Due to the home range of SRKWs spanning across the Canada-U.S. border, the recovery of this species poses a transboundary management challenge. Previous research suggests that one solution for complex environmental issues is inter-organizational collaboration. With governments unable to address transboundary natural resource management challenges on their own, diverse public policy networks involving a wide range of stakeholders may emerge. As the protection of the SRKW depends heavily on the successful collaboration between organizations and across jurisdictions, this thesis seeks to better understand the factors affecting SRKW governance in the Salish Sea. Key informant interviews (n = 32) and survey analysis (n = 35) with policy actors working for different organizations in Canada and the U.S. are used to explore how different dimensions of inter-organizational trust, perceived risk and control interact within the transboundary network to affect collaborative performance. Findings suggest that the SRKW governance network relies heavily on personal relationships and social control mechanisms to reduce the perceived risks of inter-organizational collaboration limiting network performance. The transboundary governance network is fragmented by jurisdiction, social expectations, unclear communication channels, and competition for resources, requiring careful management attention. Opportunities for integrating additional transboundary trust building activities and social control mechanisms with inclusive deliberative processes, as well as developing and supporting boundary-spanning actors in the network, are identified. Applying a multi-dimensional trust, control, perceived risk framework to analyze inter-organization collaborative performance across jurisdictions has value to transboundary conservation objectives
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".