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Record W4381615781 · doi:10.58869/iwthm2023

Proceedings of the International Workshop “Tourism and Hospitality Management” (IWTHM2023)

2023· paratext· en· W4381615781 on OpenAlexaff

Bibliographic record

Venuenot available
Typeparatext
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsInnovation Cluster (Canada)
FundersFundação para a Ciência e a Tecnologia
KeywordsHospitalityTourismHospitality management studiesBusinessComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Residents are an important stakeholder in tourism destinations. In this sense, this study aimed to identify in the cross-border zone towards tourism. A questionnaire was applied between April and May 2023 to residents older than 18 years that were randomly approached in shops, parks, restaurants, streets, and residences. A total of 470 valid questionnaires were considered for descriptive analysis of the impacts' means and standard deviation. Residents tend to perceive positively tourism in cross-border areas. The impacts that presented the highest mean were the economics, followed by the sociocultural and in the last, the environmental impacts. One of the stuady's limitations is the sample number difference between the residents of Portugal and Spain, which makes some comparative aspects between the two populations difficult. This is the first study about residents perceptions in the cross-border area of Portugal (Terras de Trás-os-Montes) and Spain (Castilla y León), characterizing the study's originality.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.927
Threshold uncertainty score0.246

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0730.017

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 head0.032
GPT teacher head0.341
Teacher spread0.308 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2023
Admission routes1
Has abstractyes

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