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Record W4385294279 · doi:10.4102/jamba.v15i1.1487

Alternatives for sustained disaster risk reduction: A re-assessment

2023· article· en· W4385294279 on OpenAlexaff
Loïc Le Dé, Louise Baumann, Annabelle Moatty, Virginie Le Masson, Faten Kikano, Mahmood Fayazi, Manuela Fernández, Isabella Tomassi, Jake Rom Cadag

Bibliographic record

VenueJàmbá Journal of Disaster Risk Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsUniversité de MontréalCampus Notre-Dame-de-FoyCollège de Rosemont
Fundersnot available
KeywordsDisaster risk reductionVulnerability (computing)FrenchContext (archaeology)HazardCitizen journalismSociologyPolitical scienceEnvironmental planningHumanitiesHistoryGeographyComputer securityLawComputer science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score0.842

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.059
GPT teacher head0.428
Teacher spread0.369 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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