Barriers to the uptake and implementation of natural flood management: A social‐ecological analysis
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 Natural flood management (NFM) is increasingly promoted as a sustainable flood risk management (FRM) option, but significant barriers remain to its implementation. We assess the barriers to uptake and implementation of NFM using an approach in which we conceptualise a catchment as a social‐ecological system. We investigate the barriers relating to multiple stakeholders, biophysical, and social components and the interactions between these different system elements. Semi‐structured interviews were undertaken with land managers and practitioners of FRM in the United Kingdom. Data were analysed using qualitative methods, including thematic coding and categorisation. Key barriers of 25 identified were: economic constraints for land managers, the current lack of scientific evidence to support NFM and current lack of governance over long‐term responsibility for NFM, which hinders future monitoring and maintenance. Practitioners within some sectors were less likely to recognise barriers noted by land managers, including cultural challenges, catchment planning concerns, and lack of perceived control. For successful wider implementation of NFM, it is crucial that practitioners recognise the barriers that land managers experience, and that projects should build monitoring programmes into their funding bids, to assess impacts on flood risk and maintenance needs and to build the evidence base to guide future NFM implementation.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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