The impacts of trust, cost and risk on collaboration in environmental governance
Bibliographic record
Abstract
1. Collaborative approaches to environmental governance are drawing increased interest in research and practice. In this article we investigate the structure and functioning of actor networks engaged in collaboration. 2. We specifically seek to advance understanding of how and why collaborative networks are formed as actors engage in addressing two broad classes of collective action problems: coordination and cooperation. It has been proposed that more risk-prone cooperative problems favor denser and more cohesive bonding network structures, whereas less risky coordination problems favor sparser and more centralized bridging structures. 3. Recent empirical findings however cast some doubts on these assumptions. In building on previous work we propose and evaluate a set of propositions in order to remedy these ambiguities. Our propositions build on the assumption that bridging structures could, if actors’ experience sufficient levels of trust in the collaborative process, adequately support both cooperation and coordination problems. 4. Our empirical investigation of four UNESCO’s Man and Biosphere Reserves gives initial support for our assumptions, and suggest that bridging structures emerge when actors have trust in the collaborative endeavor, and/or when the cost of collaborative failure is deemed low. While caution is warranted due to data limitations, our findings contribute to improved policies and guidelines on how to stimulate and facilitate more effective collaborative approaches to environmental governance. The dataset contains four networks (one per MAB reserve). The data is further described in the published paper. For each network, there are several files. The files are formatted for the program MPnet. One file per network is the sociomatrix (rows and columns are nodes, and the values in the matrix are the links between the nodes). Several other files, per network, contain node attributes (further described in the published paper). The order of the node attributes are the same as in the sociomatrices.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".