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Record W2950791502 · doi:10.33423/jabe.v21i1.1458

Correlation between Creative Tourism and Agrotourism Services Experiences: An Empirical Research in the Mexican Rural Tourism Environment

2019· article· en· W2950791502 on OpenAlexvenueno aff
Alma Cristina Gomez Macfarland, Hector Gomez Macfarland, Rohan Thompson

Bibliographic record

VenueJournal of Applied Business and Economics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsTourismRural tourismCreativityNoveltyMarketingRural areaBusinessTourism geographyPsychologyGeographyPolitical science

Abstract

fetched live from OpenAlex

There is an immediate need to promote rural areas, where Mexico’s highest poverty is concentrated, but the most potential exists for future development. The preferences of the new modern tourist are based mainly on living experiences rather than just sights and souvenirs. The creative experience as the basis of Creative Tourism can be adopted by agrotourism, a type of Rural Tourism, that consist of tourist activities which are based on experiences that natives in the rural area experience on a daily basis. Thirty tourists from 18 to 23 years of age participated in agrotourism activities such as ointment workshops, food workshops, pulque (an alcoholic beverage made from the fermented sap of the maguey- agaveplant) preparation workshops, embroidery workshops, farming workshops, and a guided trip to the ecological reserve. With the use of narratives, experience maps, and audiovisual materials, the results show that the essence of Creative Tourism was understood as active participation and creativity. Many of the participants expressed novelty as a written and graphic expression of creative tourism which enabled them to have a better overall experience. This finding suggests that agrotourism should include different aspects of creative tourism as part of a “creative experience” for those in charge of the tourism industry in Mexico.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.338
Teacher spread0.292 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations5
Published2019
Admission routes1
Has abstractyes

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