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Record W3021386691 · doi:10.4000/echogeo.19017

Organisation de la post-catastrophe après Irma à Saint-Martin

2020· article· fr· W3021386691 on OpenAlexaff
Annabelle Moatty, Delphine Grancher, Clément Virmoux, Julien Cavero

Bibliographic record

VenueEchoGéo · 2020
Typearticle
Languagefr
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsImpact
FundersAgence Nationale de la Recherche
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Le 3 septembre 2017 alors que le cyclone Irma s’approche de Saint-Martin, la rentrée scolaire est reportée sine die pour les 8 000 élèves inscrits dans les écoles publiques et les 900 personnels de l’Éducation Nationale. Le cyclone ayant endommagé une majorité de bâtiments et d’infrastructures (dont les établissements scolaires) certains élèves de l’île n’ont pas été scolarisés pendant deux mois. Nous proposons dans ce travail de considérer les récits des adolescents en tant que données susceptibles d’alimenter les retours d’expérience post-catastrophe. Notre objectif est de comprendre le vécu des adolescents et de caractériser les actions qu’ils ont menées en période post-crise. Si leur rôle est reconnu, leurs actions et les contextes de mise en œuvre méritent alors d’être précisés.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.220

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.022
GPT teacher head0.269
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations11
Published2020
Admission routes1
Has abstractyes

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