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Record W4406351239 · doi:10.4000/13350

Le Projet de paysage dans la formation des paysagistes concepteurs : le tournant des années 1990 et la génération 2000

2024· article· fr· W4406351239 on OpenAlexaff
Natalia Escar Otín, Élise Geisler, Fabienne Joliet

Bibliographic record

VenueCahiers de la recherche architecturale urbaine et paysagère · 2024
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPolitical scienceArt

Abstract

fetched live from OpenAlex

Le projet de paysage, démarche opératoire de transformation des paysages et/ou de leurs représentations, a occupé et occupe toujours une place centrale dans la formation des paysagistes concepteurs en France. Au cours des trente dernières années, l’élargissement des missions des paysagistes concepteurs au-delà de la maîtrise d’œuvre et la démultiplication des acteurs du projet d’aménagement, ainsi que la diversification de l’offre de formation et l’augmentation du flux de diplômés et du corps enseignant, tendent à renouveler les démarches du projet spatial au sein des écoles publiques de paysage. Cet article vise dans ce contexte à montrer le glissement progressif vers des enseignements qui, au-delà de former à une expertise de conception spatiale paysagère caractéristique (multiscalaire, fondée sur les spécificités du site), se déploient aussi sur les phases amont et aval du projet de paysage (des études à la gestion). Il cherche également à éclairer plus largement les évolutions pédagogiques induites par le développement des activités de recherche et la diversification du corps enseignant au sein des écoles.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.082
GPT teacher head0.340
Teacher spread0.258 · 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 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
Published2024
Admission routes1
Has abstractyes

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