The Impact of the COVID-19 Global Pandemic on the Cuban Tourism Industry and Recommendations for Cuba’s Response
Bibliographic record
Abstract
Cuba has been affected by the COVID-19 global pandemic as has most countries. The pandemic has all but shut down the tourism industry, with global flights being cancelled and governments taking drastic actions to stop the spread of the virus. The impact will especially hurt developing countries without strong economies and those heavily reliant on the tourism industry, such as Cuba. Government initiatives have included stay at home orders and temporarily closing businesses, restaurants, sports, and music venues as well as manufacturing facilities. With these shutdowns, there exists the probabilities that many businesses will not survive, but for those with sufficient cashflow, this presents opportunities for organizations and governments to re-tool, re-balance and alter their methods of operations. Cuba is different, in that they have a centralized planned economy and do have an opportunity to make significant changes to their industries which can improve the future of Cuba. The present paper looks to evaluate the impact of this on the country and the tourism industry and make economic recommendations in order for the Cuban government to move forward.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".