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Ciudades Turísticas y Desarrollo Sustentable: Benidorm, España - Cancún, México

2018· article· es· W2903540901 on OpenAlexaff
Stella Maris Arnaíz Burne, Fernanda César Arnaíz

Bibliographic record

VenueAnais Brasileiros de Estudos Turísticos - ABET · 2018
Typearticle
Languagees
FieldSocial Sciences
TopicRegional Development and Innovation
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHumanitiesPolitical scienceGeographyArt

Abstract

fetched live from OpenAlex

El objetivo de este trabajo fue comparar dos ciudades turísticas consideradas como íconos dentro de los estudios del turismo. En México, Cancún fue el ejemplo de la ciudad planificada para ser ciudad turística, hoy con más de un millón de habitantes, un 80% vive en la pobreza, que ha formado un cinturón alrededor de la ciudad y que se expresa en violencia, drogas, y todo tipo de conflictos sociales en este “paraíso tropical”. En España, Benidorm, creada también de forma planificada unos pocos años antes, hoy es igual que Cancún un destino exitoso, pero la diferencia es mayúscula en cuanto al modelo que se tomó en consideración y los resultados obtenidos; no se formó el cinturón de pobres marginales como amenaza, sino que la gente vive en sus pueblos cercanos y muchos de los que ayudaron a construir volvieron a sus tierras. Esta diferencia nos muestra dos modelos de ciudades turísticas donde la sustentabilidad es asumida, en un caso, como un discurso hueco y sus resultados son evidentes: Cancún. En el caso de Benidorm, los planificadores fueron menos ambiciosos y si bien es una ciudad de turismo masivo de clase media, las medidas tomadas han incidido en lograr un equilibrio dentro de las normas que regulan la economía de mercado, acercándose mucho a la sustentabilidad.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.714

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.324
Teacher spread0.296 · 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

Labeled directly by 2 models reading the full record.

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

Citations2
Published2018
Admission routes1
Has abstractyes

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