Analysis on Emergency and Disaster Preparedness Level of Hospitality Industry in Palu and Gorontalo Cities
Bibliographic record
Abstract
Palu and Gorontalo cities are provincial capitals located on Sulawesi Island, Indonesia. In 2018, the city of Palu was hit by the earthquake that was followed by tsunami disaster and liquefaction, causing thousands of people to die and hundreds of thousands were displaced. These types of disasters and emergencies may also potentially strike Gorontalo City due to its geographic similarity to Palu City. Natural disasters affect workers and companies, including those working in the hospitality industry. Natural disasters are not the only cause of emergencies in the hospitality industry. Emergencies in this industry can also be caused by non-natural disasters, including by social disasters. This study aimed to assess the level of emergency and disaster preparedness in the hospitality sector in Palu and Gorontalo cities using a questionnaire adapted from the APEC tourism risk management and tourism resilience index. Focus Group Discussions and interviews were also performed to discuss the implementation of emergency and disaster management in the workplace. The results of the multiple correspondence analysis of emergency preparedness and disaster management factors in hotels in Palu shows that they are relatively closer to the moderate rating. Meanwhile, the same variables in Gorontalo are found to be in the low category. Based on these results, the participation of the private sector (hospitality industry) and the government is needed to build a good synergy in disaster risk reduction programs both locally and nationally.
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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 | 0.000 | 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) | 0.000 | 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".