Social criteria for multi-criteria decision analysis in flood management
Bibliographic record
Abstract
The primary purpose of this study was to provide a set of social criteria for use in selecting among alternative flood management strategies. To obtain information on the flood-related concerns and needs of Manitoba residents, a survey questionnaire on the psychosocial impacts of the Red River flood of 1997 was administered to a sample of residents across several at-risk communities. This exploratory survey identified a broad range of impacts from the flood on individuals, families and to a more limited extent, communities. In addition to answering closed questions, respondents were given an opportunity to expand upon issues and offer additional insights if they chose. Data from the survey was organized under a number of dimensions of flood impact including' severity of flooding, evacuation impacts, economic impacts, family impacts, community impacts, knowledge, risk communication and warning, future plans, and behavior impacts.' In addition, there was particular emphasis in describing stress-related impacts which appeared as the primary dependent variables in the study, namely 'stress and stress symptoms, psychosocial symptoms of distress, and physical health impacts '. (Abstract shortened by UMI.)
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 | 0.068 | 0.126 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".