Assessing inter-organizational collaboration within the transboundary network governing the conservation of Southern Resident Killer Whales
Bibliographic record
Abstract
The Southern Resident Killer Whale (SRKW) is an endangered and charismatic species whose home range spans the Salish Sea, an ecosystem that extends across the Canada-US border. Conserving and recovering the SRKW represents a high-profile transboundary governance challenge that depends heavily on collaboration between a wide range of organizations. To better understand the factors affecting transboundary SRKW governance in the Salish Sea, this study applies a multi-dimensional trust, control, perceived risk framework to assess the inter-organizational architecture supporting collaboration. The analysis is based on key informant interviews (n=32) and surveys (n=35) conducted with policy actors working for different organizations involved with SRKW conservation and recovery in Canada and the US. Findings suggest that the SRKW governance network relies heavily on personal relationships and social control mechanisms while being fragmented by jurisdiction, social expectations, unclear communication channels, and competition for resources, requiring careful network management attention. Opportunities for integrating additional trust-building activities and social control mechanisms, combined with inclusive deliberative processes are identified. • Describes the transboundary governance network managing the endangered SRKW. • Assesses different dimensions of inter-organizational trust, risk perception and control. • Network is fragmented, with unclear communication channels and competition for resources. • The pre-collaborative environment needs greater bi-national Canada-USA policy attention. • Potential control mechanisms to enhance SRKW collaborative governance are identified.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".