MétaCan
Menu
Back to cohort
Record W2606654347 · doi:10.1080/1747423x.2017.1322154

Moderation effect of planning on housing development along the French Atlantic coast: findings from an event history hazard model

2017· article· en· W2606654347 on OpenAlexaff
Iwan Le Berre, Marius Thériault, Adeline Maulpoix, Françoise Gourmelon

Bibliographic record

VenueJournal of Land Use Science · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsUniversité Laval
FundersAgence Nationale de la Recherche
KeywordsUrban sprawlUrbanizationUrban planningModerationHazardEnvironmental planningLand useScale (ratio)Environmental resource managementGeographyBusinessEconomic growthEconomicsCivil engineeringComputer scienceEcology

Abstract

fetched live from OpenAlex

Laws and bylaws are regularly challenged for their effectiveness in containing urbanisation sprawl and, conversely, for the constraints they put on development projects. From a French case study, we used survival analysis to disentangle a complex mix of influences on the distribution of residential construction over a 42-year observation period at plot scale when almost everything changes simultaneously. We found that integrated laws and bylaws can slow down coastal urbanisation but do not stop it. Although land planning is becoming more effective, it still provides ample opportunity for residential development because other factors, like distance to existing infrastructure, exert a far stronger influence than the protection of coastal areas. Therefore, this article contributes to filling a knowledge gap about the founding role of public policies on land use dynamics.

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)0.005
metaresearch head score (Gemma)0.010
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.475

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.001

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 head0.030
GPT teacher head0.264
Teacher spread0.234 · 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

Citations14
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Land Use ScienceSame topicLand Use and Ecosystem ServicesFrench-language works237,207