When do we fly again? Managing Airlines in a Pandemic: Challenges and Recommendations
Bibliographic record
Abstract
This paper aimed to identify the challenges and propose recommendations to manage sustainability of the airline industry in a pandemic. When the World Health Organization stated Covid-19 as pandemic on March 11, 2020, governments in all countries ordered lockdowns, imposed travel restrictions, and required quarantine of 14 days for visitors and citizens upon arrival at the airport. The airlines industry came to a standstill. As Covid-19 is a recent and evolving phenomenon, the methodology employs secondary research based on data from authoritative sources such as the World Health Organization, International Civil Aviation Organization (ICAO) and International Air Transport Association (IATA) to identify the challenges. Peer reviewed research on past pandemics, especially the Severe Acute Respiratory Syndrome (SARS) in 2002-2003 help formulate recommendations to manage sustainability. The challenges are financial crisis, travel restrictions and customer distrust. The recommendations are positioning customer safety first, customer engagement, pricing strategy and collaboration with government. Limitations of the research and future research suggestions are presented
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".