Perception of Tourism Sector about Community Resilience in Puerto Vallarta, México in the Face of a Disaster Such as COVID-19
Bibliographic record
Abstract
As tourist destinations grow, they become more complex and may compromise their resilience and sustainability.Community resilience, understood as anticipating and minimizing destructive forces through adaptation or resistance, maintaining basic functions and structures during events, and recovering after these events, is an aspect that has not been extensively explored in tourism research.This study analyzed the resilience of the tourist destination Puerto Vallarta under the Hyogo Action Framework, focusing on themes such as governance, risk assessment, knowledge and education, risk management, vulnerability reduction, disaster preparedness, and response.The Delphi method was employed to evaluate resilience through the perspectives of 15 key actors.The data collected was processed using descriptive statistics, ANOVA, and factorial correspondence analysis.No significant differences were found between the groups of actors, and it is concluded that the destination is not resilient.Its recovery from the COVID-19 crisis is expected to be slow due to a lack of strategies in this regard.This research aims to contribute to the understanding of community resilience as perceived by stakeholders in a consolidated tourism destination.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".