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Record W7038594695

Impacts des mesures de préservation des sites naturels exceptionnels

2016· other· fr· W7038594695 on OpenAlexaboutno aff

Bibliographic record

VenueOskar-Bordeaux (Universite de Bordeaux) · 2016
Typeother
Languagefr
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsNova scotiaContext (archaeology)Public investmentConciliation
DOInot available

Abstract

fetched live from OpenAlex

La mise en tourisme des patrimoines, et notamment des patrimoines naturels, met au jour des enjeux politiques et économiques autour de lieux convoités et des grands sites naturels. Dans cette optique se pose de façon conjointe à l’innovation technologique ou touristique de gestion des flux et de leurs retombées économiques, sociales, environnementales, le problème de la gouvernance et notamment de l’anticipation dans des contextes variés de prise de décision par les décideurs. À l’heure de la transition touristique, la question de la conciliation entre la protection des milieux et la fréquentation touristique demeure posée. La gestion des sites classés ou inscrits bénéficiant d’une forte notoriété se doit donc de combiner la protection (interdiction générale de modifier l’aspect des lieux) et la valorisation, essentielles à l’économie touristique dans un contexte souvent contraint. La « restauration de l’esprit des lieux » doit également permettre d’améliorer les conditions d’ouverture au public et la qualité de la visite. Quelle est l’efficacité, en la matière, des mesures mises en œuvre ? Quel est l’impact des actions visant à réduire l’accessibilité au site non seulement sur le plan économique, mais aussi social, politique et environnemental ? Comment apprécier ces impacts en termes tant quantitatifs que qualitatifs ?

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.377
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0020.004
Scholarly communication0.0010.004
Open science0.0030.001
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0780.028

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 head0.015
GPT teacher head0.296
Teacher spread0.282 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

Explore more

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