MétaCan
Menu
Back to cohort
Record W3199039648 · doi:10.4000/tourisme.3575

La mise en tourisme du patrimoine viticole : l’œnotourisme dans les vignobles du Cap Bon (Tunisie)

2021· article· fr· W3199039648 on OpenAlexaff
Mohamed Souissi

Bibliographic record

VenueMondes du tourisme · 2021
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsCanadian Association of Physicists
Fundersnot available
KeywordsWineTourismGeographyWine grapeViticultureBusinessArtArchaeologyVisual arts

Abstract

fetched live from OpenAlex

Le développement de l’œnotourisme et la possibilité d’intégrer la dimension paysagère et patrimoniale dans les activités touristiques placent les vignobles de la péninsule du Cap Bon (Tunisie) en excellente position pour associer terroir, patrimoine et paysage. La mise en tourisme du patrimoine viticole et des ressources naturelles non liées exclusivement au soleil et à la mer trouvent ainsi un nouveau sens (gastronomie locale, produits de terroirs, routes viticoles, labellisation). Malgré l’importance des aménités touristiques viticoles, l’œnotourisme au Cap Bon ne représente pas encore une réelle offre alternative, ou du moins, sa promotion n’est pas ou peu visible. L’objet de cet article est de comprendre l’émergence d’une fréquentation œnotouristique à partir d’entretiens avec des acteurs locaux. Il vise également à démontrer dans quelles conditions le Cap Bon pourrait intégrer la valorisation de son paysage et de son patrimoine viticole colonial aux politiques de diversification du produit touristique tunisien.

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, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.010
GPT teacher head0.205
Teacher spread0.195 · 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
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 venueMondes du tourismeSame topicWine Industry and TourismFrench-language works237,207