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Record W2970489474 · doi:10.5539/jpl.v12n5p71

French Policy in the Sphere of Tourism

2019· article· en· W2970489474 on OpenAlexvenueno aff
Ekaterina Vladislavovna Kolupaeva, Liliya R. Galimzyanova

Bibliographic record

VenueJournal of Politics and Law · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Systems and Logistics Management
Canadian institutionsnot available
FundersKazan Federal University
KeywordsTourismPromotion (chess)State (computer science)Christian ministryMinistry of Foreign AffairsTourism geographyWork (physics)EconomyEconomic growthSustainable developmentEconomic policyPolitical scienceBusinessEconomicsPublic administrationPoliticsLawEngineering

Abstract

fetched live from OpenAlex

In this paper we present the current situation of France in the field of tourism and describe the main state organizations that carry on business in the sphere of tourism development in the French Republic. We also give examples of the main events delivered by these institutions for the sustainable development of the tourism industry in France. Today in France there are several state structural units that are full of vitality in this direction. Of these, the following departments and organizations were considered: Ministry of Foreign Affairs and International Development; Ministry of Commerce and Finance; Interagency Committee on Tourism, Tourism Promotion Council, Atout France, etc. Thanks to the active work carried out at the state level, France today holds one of the leading positions among the countries to be most frequently visited by tourists. Moreover, the results of this smart policy are the annual income from the development of the tourism industry in France, which, in turn, significantly affects the economic welfare of the country. Thus, a carefully thought-out state policy in the field of tourism has a favorable effect on the socio-economic condition of the country as a whole.

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)0.003
metaresearch head score (Gemma)0.004
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: Other · Consensus signal: Other
Teacher disagreement score0.173
Threshold uncertainty score0.344

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0040.003
Scholarly communication0.0080.002
Open science0.0000.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.022
GPT teacher head0.226
Teacher spread0.204 · 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
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

Citations1
Published2019
Admission routes1
Has abstractyes

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