Governance Spaces for Sustainable River Management
Bibliographic record
Abstract
Abstract There is widely documented evidence that rivers are one of the most degraded ecosystem types on the planet. As a consequence, concerted efforts have been made to improve the health of river systems in many parts of the world. Moves towards sustainable management approaches reflect transitions beyond the imposition of ‘command‐and‐control’ approaches towards ecosystem‐framed applications. Although this transition is now well‐understood in intellectual terms, there is little evidence of a genuine shift in practice and associated outcomes. Governance frameworks underpinning management practices have been identified as a key limitation in catalysing this transition. This paper provides an overview of governance frameworks and practices which underpin river management goals. Middle‐ground governance frameworks that facilitate the interaction of top‐down and bottom‐up approaches are promoted as this structure allows for values and processes operating across multiple spatial and temporal scales to be included in management. Case studies from New Zealand, Canada and England are used to demonstrate the diversity of governance spaces that middle‐ground initiatives can occupy, reflecting the unique socio‐ecological and institutional trajectory of any given catchment. Middle‐ground organisations at the catchment scale provide a focal meeting point to pool resources and set goals for decentralised, reflexive structures. This transition in practice is critical if contemporary top‐down approaches are to be modified to foster adaptive ecosystem‐based applications that incorporate participatory decision‐making at a catchment scale. These considerations are vital if appropriate platforms are to be established to maximise efforts for sustainable river management.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".