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Record W3038659906 · doi:10.1522/revueot.v29n2.1156

Gestion des catastrophes naturelles en sol québécois : rendre socialement et écologiquement responsables les processus de développement des territoires affligés

2020· article· fr· W3038659906 on OpenAlexaffvenueabout
Diane Alalouf-Hall, Jean-Marc Fontan

Bibliographic record

VenueRevue Organisations & territoires · 2020
Typearticle
Languagefr
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Dans le contexte où les changements environnementaux induisent une augmentation des aléas météorologiques, les défis d’adaptation et de sécurité civile se multiplient. Cet article a été rédigé dans le cadre d’une thèse portant sur la réponse standardisée des acteurs lors de catastrophes d’origine naturelle. Nous nous intéressons à la gestion québécoise des catastrophes dites naturelles. D’abord, nous aborderons la question des limites juridiques de l’État québécois. Puis, nous nous pencherons sur la littérature portant sur le développement territorial. Cela nous permettra d’identifier la place occupée par la question de la prévention en matière de catastrophes naturelles. Enfin, nous présenterons un état de situation sur la question des zones inondables au Québec, ce qui nous permettra d’indiquer en quoi des mesures d’atténuation de la présence humaine habitée ont été pensées, mais peu prises en compte. Nous concluons en indiquant que le Québec ne peut se contenter d’innovations technologiques structurantes pour assurer un aménagement territorial qui soit socialement et écologiquement responsable face aux catastrophes naturelles.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.744
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.295
Teacher spread0.261 · 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.

Study designNot applicable
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
Published2020
Admission routes3
Has abstractyes

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