Reconceptualizing disaster phases through a<i>Metis-</i>based approach
Bibliographic record
Abstract
Purpose This paper aims to call for change in disaster research through ametis-based approach that values practical skills and knowledge (vs technical knowledge) derived from responding to ongoing changes in the natural and human environment. Design/methodology/approach This paper is based onmetisfrom Miami-Dade County that is prone to an array of climate-related disasters.Metisis supplemented by a review of secondary sources (e.g. newspaper articles, government reports). Findings There is a need to reconceptualize disaster phases in disaster research—preparedness, response, recovery and mitigation. For many members of marginalized communities of color, this paper depicts preparedness and mitigation as luxuries and response as a time of worry about financial obligations and survival after the disaster. It suggests that even communities that are not on a hurricane's path could have post-disaster experiences. It also highlights ongoing risks to marginalized communities' physical and mental well-being that are in addition to the mental health impacts of the disaster during the recovery phase. Originality/value This paper's originality is twofold: (1) underlining the importance ofmetis, a less studied and understood concept in disaster risk reduction, prevention and management literature and (2) questioning disaster researchers' technical knowledge with respect to each of the four disaster phases in light ofmetis.
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 | 0.055 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".