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Record W4319313216 · doi:10.37811/cl_rcm.v7i1.4615

Calidad estética del paisaje y su implicancia en la intención de visita con fines de ecoturismo. El caso del parque nacional cerros de Amotape -Perú

2023· article· es· W4319313216 on OpenAlexaff
Mag. Pablo Esteban Marticorena Landauro, Luy Navarrete Wayki Alfredo, Armina Isabel Morán Baca, Luis Alberto Puño Rojas, Mag. Elizabet Monica Obeso Orbegoso

Bibliographic record

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

Abstract

fetched live from OpenAlex

La investigación tuvo como objetivo determinar la implicancia de la valoración social de la calidad estética del paisaje en la intención de realizar ecoturismo en el Parque Nacional Cerros de Amotape (PNCA). Se aplicó un cuestionario de encuesta 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. Para evaluar la calidad visual del paisaje, se utilizó el método directo de subjetividad representativa (MDS) y se evaluó la intención de realizar ecoturismo en el PNCA mediante una escala Likert. Se utilizó estadística no paramétrica para evaluar la relación entre ambas variables. Los hallazgos de este estudio evidencian que existe una asociación positiva moderada entre calidad estética del 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 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)0.001
metaresearch head score (Gemma)0.003
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 score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.022
GPT teacher head0.355
Teacher spread0.333 · 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
Published2023
Admission routes1
Has abstractyes

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