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Record W4366768955 · doi:10.37811/cl_rcm.v7i2.5610

Emociones que genera el paisaje y su implicancia como potencial Ecoturístico. El caso del Parque Nacional Cerros de Amotape, Perú

2023· article· es· W4366768955 on OpenAlexaff
Mag. Pablo Esteban Marticorena Landauro, Wilser Renán Castillo Carranza, Armina Isabel Morán Baca, Luis Alberto Puño Rojas

Bibliographic record

VenueCiencia Latina Revista Científica Multidisciplinar · 2023
Typearticle
Languagees
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsWSP (Canada)
Fundersnot available
KeywordsHumanitiesGeographyArt

Abstract

fetched live from OpenAlex

El estudio tuvo como objetivo determinar si las emociones que genera paisaje tienen implicancia en el potencial ecoturístico del Parque Nacional Cerros de Amotape (PNCA), Tumbes, Perú; con base en la perspectiva de la demanda turística potencial. El tipo de investigación fue no experimental, correlacional, transversal y prospectiva, se aplicó un cuestionario con escala Likert a una muestra de 383 ciudadanos peruanos residentes en áreas urbanas del departamento de Tumbes, en edad de trabajar y con nivel educativo superior alcanzado. Se utilizó el método directo de subjetividad representativa y una adaptación de la escala de emociones planteada por Fredrickson (2009) para medir las emociones que genera el paisaje y se evaluó el potencial turístico en función de la actitud de la demanda potencial hacia las instalaciones y servicios ecoturísticos y su intención de realizar ecoturismo en el PNCA. Los hallazgos evidencian que existe una asociación positiva entre las emociones que genera el paisaje y una actitud favorable hacia la práctica de actividades ecoturísticas en el PNCA, siendo el paisaje un elemento motivador de desplazamientos turísticos desde las zonas urbanas hacia entornos rurales.

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)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.005
Science and technology studies0.0060.003
Scholarly communication0.0020.001
Open science0.0040.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.003

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 head0.031
GPT teacher head0.354
Teacher spread0.323 · 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
Published2023
Admission routes1
Has abstractyes

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