Governance Spaces for Sustainable River Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.001 | 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 it