Flood risk management and governance: A bibliometric review of the literature
Bibliographic record
Abstract
Abstract The study of flood management has experienced a paradigmatic shift over the past two decades. Particularly notable are the embracement of flood risk management (FRM) and comparative analysis of flood risk governance (FRG), meaning the complex institutional arrangements that shape the behavior of state and societal actors concerning FRM. Thousands of publications have addressed these themes, and this field of study is ripe for a systematic analysis that consolidates and structures this rapidly evolving literature. This study employed a bibliometric methodology to analyze the metadata (including authorship, keywords, abstracts, and citations) of 3059 such publications. The results reveal that both FRM and FRG scholarship have expanded over the past two decades; the United Kingdom and the Netherlands are the most prominent countries of origin, a small number of prolific authors stands out as major contributors, and a relatively small number of journals dominate as publication venues. The text mining results reveal that the bodies of FRM and FRG scholarship are highly correlated but yet differ in core subject matter, as demonstrated by the unique keywords found in the analysis. The findings are useful for researchers seeking relevant clusters for study and therefore offers reference value for future research and practice.
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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.131 | 0.145 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".