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Record W2398878751

Turismo en Centroamérica. Un diagnóstico para el debate. Cañada, Ernest (Coord.), Managua (Nicaragua). Ed. Enlace, 2013. 164 p.

2013· article· es· W2398878751 on OpenAlexaboutno aff
Enrique Navarro Jurado

Bibliographic record

VenueInvestigaciones Turísticas · 2013
Typearticle
Languagees
FieldSocial Sciences
TopicRegional Development and Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArtGeographyCartography
DOInot available

Abstract

fetched live from OpenAlex

El crecimiento de las actividades turisticas en Centroamerica posiciona a esta region como uno de los destinos emergentes mas interesante. Pese a la competencia de otros destinos cercanos (Islas caribenas, Mexico, Brasil…) las perspectivas de futuro parecen prometedoras y, por ello, todos los paises centroamericanos apuestan por el turismo como un sector clave para alcanzar mayores cotas de desarrollo. Sin embargo, ya se ha demostrado en multiples ocasiones que el crecimiento economico no conlleva siempre un mayor bienestar para los ciudadanos. El hecho es que se ha insistido en adoctrinarnos con la idea de que la logica del crecimiento economico sirve como argumento para todo “como si esto automaticamente fuera a traducirse en un mayor bienestar general y no tuviera otras consecuencias”; y efectivamente, el desarrollo dependera del modelo turistico que se implante, del modo en que se repartan los beneficios, del horizonte temporal en que se pretendan alcanzar los objetivos... Dados estos condicionantes, las preguntas obligadas -que he ido aprendiendo de mis colegas latinoamericanos- ahora son ?Para que queremos turismo si no es para mejorar el nivel de vida de la poblacion??Quienes son los maximos beneficiarios de la industria turistica? ?Hay otras maneras de hacer turismo? ?Puede el turista hacer algo para mejorar el modelo? Estas y otras preguntas son las cuestiones que plantea esta publicacion coordinada por Canada.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.010

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 head0.283
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; both teacher heads agree on what is shown here.

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
Published2013
Admission routes1
Has abstractyes

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