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Record W2917859880

La Calidad del Servicio que ofrece Cuba al Turismo Canadiense / The Quality of Service offered by Cuba to Canadian Tourism

2019· article· es· W2917859880 on OpenAlexaboutno aff
Tania Caridad Carrazana Amador

Bibliographic record

VenueRevista Economía y Desarrollo (Impresa) · 2019
Typearticle
Languagees
FieldSocial Sciences
TopicUrbanism, Landscape, and Tourism Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceTourismArtLaw
DOInot available

Abstract

fetched live from OpenAlex

Se valora el comportamiento del turismo canadiense, su crecimiento de 1994 hasta 2015 y su decrecimiento desde 2016, en contraste con el crecimiento de otros mercados emisores y del turismo canadiense hacia Mexico y Republica Dominicana. Se analizan los motivos de esta tendencia. Se formulan elementos que contribuyen a trazar estrategias para que el sistema turistico cubano pueda conservar su dinamica al crecimiento. Se enfatiza la necesidad de elevar la calidad del servicio que se ofrece al turista canadiense para lograr su satisfaccion, asi como la urgencia de continuar estimulando este mercado historico y que Cuba tiene potencialidades para recuperarlo. / The behavior of Canadian tourism, its growth from 1994 to 2015 and its decrease since 2016 is valued, in contrast to the growth of other markets and Canadian tourism to Mexico and the Dominican Republic. The reasons for this trend are analyzed. Elements are formulated that help to draw up strategies so that the Cuban tourist system can keep its growth dynamic. The need to raise the quality of the service offered to Canadian tourists to achieve their satisfaction is emphasized, as well as the urgency to continue stimulating this historic market and that Cuba has the potential to recover it.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.079
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.018
GPT teacher head0.282
Teacher spread0.265 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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