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Record W6997224552

Un regard psycho-socio-environnemental sur les risques côtiers : une étude de cas en France et au Canada

2021· dissertation· fr· W6997224552 on OpenAlexaboutno aff

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2021
Typedissertation
Languagefr
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsnot available
Fundersnot available
KeywordsTourismUrbanizationContext (archaeology)PoliticsWork (physics)Sociocultural evolutionCivil societyClimate change
DOInot available

Abstract

fetched live from OpenAlex

Over the last few decades, the occupation of coastal areas has intensified throughout the world (Meur-Ferec & Morel, 2004). These areas have become increasingly artificial, whether due to the development of tourist infrastructures or the multiplication of second homes. In a context marked by climate change, manifested among other things by a rise in sea level, these areas are subject to new challenges in terms of adaptation (IPCC, 2019).The issue of coastal risks (submersion and marine erosion) is a pressing one and concerns different sectors of society, whether it be the economic sphere (e.g. maintaining the tourist offer), the political world (e.g. managing urbanization and risks) or civil society (e.g. the users and inhabitants of these areas).Our research focuses on the social representations of these risks, mobilized by the individuals who live and/or work in these coastal territories. It was conducted in an international context, in several territories at risk in France and Canada. Different survey techniques were used: interviews, questionnaires, word association tasks, and content analysis of press articles. Our results show, contrary to the dominant scientific considerations which claim to give an objective and realistic evaluation of the dangers, the existence of a social construction of the risk dependent on an environmental and sociocultural context.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.252
Teacher spread0.242 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueHAL (Le Centre pour la Communication Scientifique Directe)Same topicRisk Perception and ManagementFrench-language works237,207