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Record W4324336885 · doi:10.18280/ijsdp.180202

Organizational and Legal Mechanisms and Limitations of the Participation of the Local Population in Developing the Tourist Attractiveness of the Territories

2023· article· en· W4324336885 on OpenAlexvenueno aff
Elena V. Frolova, Olga V. Rogach, Anton Ostrovskii, Vladimir Savinkov

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsnot available
Fundersnot available
KeywordsAttractivenessTourismSubsidyLocal governmentPopulationBusinessLocal communityGovernment (linguistics)Public relationsMarketingEconomic growthPolitical sciencePublic administrationSociologyEconomicsPsychologyLaw

Abstract

fetched live from OpenAlex

The research problem lies in the low involvement of local residents in the processes of developing the tourist attractiveness of the territories, insufficient support for the initiatives of the authorities to develop domestic tourism. The authors in their study set the goal - to study the possibilities and barriers to attracting representatives of local communities to the development of tourism. The key research method is a questionnaire survey of the population (N=732). The results of the study showed that the presence of social alienation in the interaction of the government and the population, as well as a low level of trust in local governments significantly limit the practice of involving residents in the development of tourist attractiveness of the territory. Additional barriers are the following: information vacuum of the local community on the development of domestic tourism; organizational and legal dysfunctions of business support. There is a need provision of legal and organizational support to active representatives of the local community, subsidizing, reducing the tax burden, grant support.

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)0.009
metaresearch head score (Gemma)0.027
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 score0.011
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.040
GPT teacher head0.298
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 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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Sustainable Development and PlanningSame topicRegional Socio-Economic Development TrendsFrench-language works237,207