DEMAND FOR INTERNATIONAL TOURISM IN TIMES OF COVID-19 IN THE PUNO-PERU REGION
Bibliographic record
Abstract
<strong>Abstract: </strong>The tourism industry is important in the Puno region as hundreds of people benefit from it. However, in the face of the measure of social isolation resulting from the global pandemic of COVID-19, this sector of the economy was drastically damaged, since for the second quarter of 2020 the total arrivals fell by 100%. In this sense, it is necessary to know the future arrivals in the absence of the pandemic to estimate the losses and reactivation strategies. The objective of the work is to know the behavior of the demand for international tourism in the Puno region and forecast it into the future using time series models with monthly information from the period 2003 to 2019. The results of the investigation revealed that the model is the most efficient for the modeling and forecasting of tourism in Puno.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".