Accountability in Networked Governance: Learning from a case of landscape‐scale forest conservation
Bibliographic record
Abstract
Abstract Despite incredible strides in transboundary collaborative conservation, many challenges remain. A networked governance approach recognizes a diverse pool of participants, linkages across multiple levels of organization and the diffusion of authority horizontally across spatial scales. Much is understood about the basic form and function of networked governance, namely the ways in which it overcomes weaknesses of traditional hierarchical structures, but less is known about the democratic quality of newer forms of governance. There are implications for traditional forms of accountability for the practice of network governance. They are not lost but their dimensions are changed, hinging less on punishment and more on reward. To examine this dynamic, we use a mixed‐methods approach and grounded theory to explore the social relationships that make up a conservation network in the United States and Canada. Interview analysis from the Roundtable on the Crown of the Continent suggests that accountability comes through authentic engagement, is based on a ‘logic of appropriateness’ rooted in normative persuasion and still draws from traditional hierarchy. Social network analysis shows positions of brokerage and bridging help to maintain network connections between actors. Leveraging these characteristics of the network and the relationships formed through the process of landscape forest governance, we suggest there may be an overall net gain in accountability. Copyright © 2015 John Wiley & Sons, Ltd and ERP Environment
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".