Barriers to the uptake and implementation of natural flood management: A social‐ecological analysis
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.
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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".