Responding to Hurricanes Irma and Maria: An Exploration of Puerto Rico Tourism's Image Repair Efforts
Bibliographic record
Abstract
Tourists’ perceived image of a destination, including perceptions of safety and risk, play important roles in deciding whether or not to visit a destination. When crises strike a tourist destination, tourism organizations must work towards restoring their images to a favourable state. In the fall of 2017, hurricanes Irma and Maria swept through the Caribbean with devastating consequences. Puerto Rico, whose tourism industry plays a vital role in its economy, was particularly devastated by the hurricanes. In the wake of hurricanes Irma and Maria, Puerto Rico’s tourism sector was in a state of crisis whereby its economic health depended on tourism revenue. Since image plays a vital role in the health of Puerto Rico’s tourism industry, this Major Research Paper (MRP) seeks to analyze the use of image repair strategies employed by Puerto Rico’s official tourism organization, See Puerto Rico, across multiple online platforms and across varying stages of the hurricane crisis. Drawing from image repair theory (Benoit, 1997), texts and images found on See Puerto Rico’s website and Facebook page were analyzed by identifying the presence of denial, evading responsibility, reducing offensiveness, corrective action, and mortification strategies with the addition of informational and suffering strategies. Findings indicate that See Puerto Rico primarily employed bolstering, informational, corrective action, minimization, and suffering strategies throughout its website and Facebook page. Puerto Rico strived to restore its image by providing tourists select pieces of information regarding the status of Puerto Rico’s tourism industry and by showcasing the positive attributes of the islands. Part of See Puerto Rico’s image repair efforts involved ignoring the hurricanes and minimizing their seriousness and impacts to reassure tourists that they could still travel to Puerto Rico. The application of image repair theory in this study suggests that it can be used to help understand tourism organizations’ crisis responses to natural disasters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".