Factors underlying the concept of risk acceptance in the context of flood-prone land use
Bibliographic record
Abstract
The determination of acceptable risk levels for planning purposes is critical for policymakers concerned with floodplain management and safety issues; including the formulation of flood-prone land use policy and risk communication strategies. However, the concept of flood risk acceptance remains vague and is not yet fully understood in terms of how it is conceived and rationalised by the individuals engaged in flood-prone land use and development. In general terms, risk acceptance involves a complex weighing up of a range of influential factors that have evolved based on three key models: revealed preferences, expressed preferences and implied preferences. By investigating these models within the broader theoretical context of flood risk-related research, this paper, in essence, categorises six typologies that describe individuals' psychophysical/cognitive states when they face the risk, respond to it and determine its acceptability contextualised within the process of flood-prone land use change. The paper then focuses on identifying the key barriers influencing the adoption of an informed, consultative approach to acceptable flood risk assessment and governance.
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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 | 0.001 | 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) | 0.000 | 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".