Bibliographic record
Abstract
Abstract Natural hazards are a complex governance problem. Managing the risks associated with natural hazards requires action at all scales—from household to national—but coordinating these nested responses to achieve a vertically cohesive course of action is challenging. Moreover, though governments have the legal authority and legitimacy to mandate or facilitate natural hazard risk reduction, non-governmental actors such as business firms, industry associations, research organizations and non-profit organizations hold much of the pertinent knowledge and resources. This interdependence demands horizontal collaboration, but coordinating risk reduction across organizational divides is fraught with challenges and requires skillful leadership. Flood risk management (FRM)—an integrated strategy to reduce the likelihood and impacts of flooding—demonstrates the governance challenge presented by natural hazards. By engaging stakeholders, coordinating public and private efforts, and employing a diversity of policy instruments, FRM can strengthen societal resilience, achieve greater efficiency, and enhance the legitimacy of decisions and actions to reduce flood risk. Implementing FRM, however, requires supportive flood risk governance arrangements that facilitate vertical and horizontal policy coordination by establishing strategic goals, negotiating roles and responsibilities, aligning policy instruments, and allocating resources.
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 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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".