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Record W3022560099

Tourism of conventions, a public policy, as a source of income, in Mérida, Yucatán, México

2020· article· en· W3022560099 on OpenAlexaboutno aff
María del Carmen Ancona Alcocer

Bibliographic record

VenueJournal of Tourism and Heritage Research: JTHR · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicConferences and Exhibitions Management
Canadian institutionsnot available
Fundersnot available
KeywordsTourismPoliticsEntertainmentConventionBusinessVariety (cybernetics)PovertyDestinationsEconomic growthGeographyMarketingEconomyPolitical scienceEconomics
DOInot available

Abstract

fetched live from OpenAlex

In Mexican companies, one way to retain their employees, to attract more customers, suppliers, share knowledge and exchange of ideas is to hold Conventions or Expos, therefore, convention tourism has become an important segment in the marketing strategies of certain destinations in Mexico. Its direct and indirect effects on local economies are not only valued in terms of economic impact, but also for their image effect on the city where it is carried out. , these activities that are carried out around them such as: educational, political, business and entertainment are a source of financing for a locality, thus, the city of Merida, Yucatan has become one of the favorite places for the realization of these.A descriptive type methodology was used through secondary sources. It is concluded that convection tourism has grown exponentially in this city, due to its worldwide recognition for the security it presents when it occupies the first place in terms of security in the country and second in the American continent afterwards from Quebec City, in Canada, it has infrastructure and hotel capacity, as well as offering its wide gastronomic variety and pre-hispanic places, and the ceremonial centers of the mayan culture. This makes it a consolidated city in competing with any recognized destination in the world in the realization of events.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.417
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.126
GPT teacher head0.394
Teacher spread0.268 · 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 teacher head, 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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Same venueJournal of Tourism and Heritage Research: JTHRSame topicConferences and Exhibitions ManagementFrench-language works237,207