Alternatives for sustained disaster risk reduction: A re-assessment
Bibliographic record
Abstract
Alternatives for sustained disaster risk reduction' was published in 2010 by Francophone and Anglophone researchers as a critique on the way disasters were studied and disaster risk reduction handled in the Francophone sphere. The authors criticized the dominant Francophone approach for being heavily hazard-centred and called for more emphasis on vulnerability to understand disasters and foster disaster risk reduction - a shift that had already taken place in the Anglophone disaster literature. Twelve years later, this paper draws upon a bibliographic analysis to examine if the arguments developed in the 2010 publication have stem attention in the Francophone disaster literature. Contribution: The article finds that the shift towards the vulnerability paradigm has, to some extent, happened but took much longer in the French context than in the Spanish language and the Asian disaster literature. The article emphasises the need for a re-assessment of our practices and study of disasters, including reflections on what disasters are studied, how, by whom, and for whom. Eventually, alternatives for sustained disaster risk reduction now and in the future might include drawing upon more diverse ontologies and epistemologies that are pertinent locally, considering local people as co-researchers though participatory methods, and empowering local Francophone researchers to play a greater role in researching disasters and leading disaster risk reduction in their own localities.
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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 | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".