Mobilidade de Turistas Internacionais: Uma Comparação entre Dados Oficiais e de LBSN
Bibliographic record
Abstract
O estudo do comportamento de turistas é estratégico para melhoria dos serviços nesse competitivo segmento econômico. Trabalhos atuais geralmente exploram essa questão usando dados tradicionais, como questionários. Esse tipo de fonte fornece informações valiosas, no entanto, sofre com escalabilidade e abrangência. Uma fonte alternativa que minimiza esses problemas é obtida pelas redes sociais baseadas em localização (LBSNs). No entanto, para o uso apropriado desses dados é necessário averiguar se o comportamento capturado nessas redes reflete de maneira satisfatória o comportamento real medido com dados tradicionais. Assim, o presente trabalho visa validar se o fluxo internacional de turistas capturado com uma LBSN reflete de maneira satisfatória o comportamento real medido com dados tradicionais. Resultados iniciais sugerem que os dados LBSNs representam notavelmente bem o comportamento estudado e que podem habilitar pesquisas sobre a mobilidade desses turistas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".