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Record W4328023748 · doi:10.7202/1097160ar

Territorial planning and adaptation to global changes. A critical analysis of the French State doctrine on the coast of the Gulf of Lion

2023· article· en· W4328023748 on OpenAlexvenueno aff
Anne Brun, Llewella Maléfant

Bibliographic record

VenueCanadian Journal of Regional Science · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDoctrineDecentralizationCoastal erosionUrban planningGeographyState (computer science)Environmental planningSubmersion (mathematics)World War IIEnvironmental resource managementEconomyPolitical scienceArchaeologyCivil engineeringLawOceanographyEngineeringShoreEconomics

Abstract

fetched live from OpenAlex

The Mediterranean coast condition is worrying. The Gulf of Lion coast lower and sandy areas, densely urbanized, are particularly exposed to erosion. In addition, this coastline will experience by 2100 an increase in salt water intrusions, floods by marine submersion and damage to infrastructure due to sea level rise. The State first “left it to the developers” at the end of the 19 th century, then decided to build new seaside resorts within the ‘Mission Racine’ the framework in the second half of the 20 th century. The State now defends a “retreat doctrine” by relocating activities and people further away from the sea. However, this doctrine is opposed by economic actors and especially by local elected representatives, who have become the real architects of planning policies as a result of the 80’ decentralisation. The adaptation of this very touristy coast, which was an international laboratory of architectural and urban innovation during the 1960s, therefore seems compromised. Neither the local planning tools nor even the regional coastal management strategy can reconcile economic development and resilient urban planning in the absence of coastal governance.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.002
Scholarly communication0.0000.000
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 head0.080
GPT teacher head0.312
Teacher spread0.232 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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