Environmental governance: A practical framework to guide design, evaluation, and analysis
Bibliographic record
Abstract
Governance is one of the most important factors for ensuring effective environmental management and conservation actions. Yet, there is still a relative paucity of comprehensive and practicable guidance that can be used to frame the evaluation, design, and analysis of systems of environmental governance. This conceptual review and synthesis article seeks to addresses this problem through resituating the broad body of governance literature into a practical framework for environmental governance. Our framework builds on a rich history of governance scholarship to propose that environmental governance has four general aims or objectives – to be effective, to be equitable, to be responsive, and to be robust. Each of these four objectives need to be considered simultaneously across the institutional, structural, and procedural elements of environmental governance. Through a review of the literature, we developed a set of attributes for each of these objectives and relate these to the overall capacity, functioning, and performance of environmental governance. Our aim is to provide a practical and adaptable framework that can be applied to the design, evaluation, and analysis of environmental governance in different social and political contexts, to diverse environmental problems and modes of governance, and at a range of scales.
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 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 | 0.162 | 0.143 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".