Probing the relationship between ecosystem perceptions and approaches to environmental governance: an exploratory content analysis of seven water dilemmas
Bibliographic record
Abstract
Addressing wicked ‘water dilemmas’ requires an understanding of the context within which they are embedded. This study explored perceptions of the ecosystem in terms of resilience and the governance approaches employed through a content analysis of documents from seven case studies across the globe. Analytical constructs developed for resilience and governance approaches guided the exploration. Multiple resilience types were present in documents for each case, but few patterns emerged across cases. Governance approaches were strongly focused on state approaches in most cases. A relationship between resilience type and governance approach was not clear; however, a pattern emerged between the presence of the social–ecological resilience type and non-state-centred governance forms. The type of author (government, non-government) or the type of document (research and advisory, descriptive) were not found to mediate the findings as resilience framings varied considerably and state governance approaches were emphasised throughout. As the findings stand in contrast to contemporary scholarship on understanding ecosystems and environmental governance they raise important issues to which individuals must be cognizant when accessing documents for guidance. They also open avenues for future investigation of water dilemmas at the nexus of theory, policy and practice.
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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 | 0.021 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".