On the architecture of collaboration in inter-organizational natural resource management networks
Bibliographic record
Abstract
This paper reviews the architecture of collaboration that exists within inter-organizational natural resource management (NRM) networks. It presents an integrative conceptual framework designed to help operationalize the multi-level interactions that occur between different dimensions of trust, risk perception, and control as key concepts in inter-organizational collaboration. The objective is to identify and justify a series of propositions considered suitable for assessing inter-organizational NRM network collaboration through empirical work. Such an integrative conceptualization goes beyond the existing trust scholarship related to collaborative NRM, and, we argue, offers a useful starting point for further exploring some of the 'inner' social dynamics affecting collaborative performance using complex systems thinking. To help establish the relevance of the conceptual framework to transboundary resource governance, a survey operationalizing different dimensions of trust, perceived risk, and control is piloted in the Salish Sea, an ecosystem that spans the Canada-US border between British Columbia and Washington State. Key challenges associated with operationalizing the framework and future research needs 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".