On the frontiers of collaboration and conflict: how context influences the success of collaboration
Bibliographic record
Abstract
The increasing scale and interconnection of many environmental challenges – from climate change to land use – has resulted in the need to collaborate across borders and boundaries of all types. Traditional centralized, top-down and sectoral approaches to governance of single-issue areas or species within social-ecological systems often have limited potential to alleviate issues that go beyond their jurisdiction. As a result, collaborative governance approaches have come to the forefront. A great deal of past research has examined the conditions under which collaborative efforts are likely to achieve desired outcomes. However, few studies have analyzed how the means to achieve successful collaborative outcomes differ based on context when examined across multiple studies. In this research, we begin to chart a means for doing this. Building onto a Context-Mechanism-Outcome (CMO) Framework, we provide a coding manual to analyse how contextual variables mediate the effects of mechanism variables on outcomes of the collaborative governance of social-ecological systems. Through the examination of four cases, we provide a proof-of-concept assessment and show the utility of the CMO framework and coding manual to draw comparisons across cases for understanding how collaborative outcomes are contingent on the social-ecological context in which they occur.
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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 | 0.058 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.014 | 0.035 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.002 | 0.004 |
| 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".