Air transportation and COVID-19: A tale with three episodes
Bibliographic record
Abstract
The COVID-19 pandemic has highlighted the extreme vulnerability of our aviation system towards external disruptions. While there have been several earlier aviation-related crises, the impact of COVID-19 is unmatched in the history of modern aviation. Accordingly, a better understanding of the mechanisms and ramifications of this pandemic is instrumental for preparing towards future external disruptions. The contribution of our study is threefold. First, we dissect the disruptive impact of the pandemic on the scientific literature and extract the major trends and insights. Given the wide range of related venues and the extent of disruption, there have been many studies published in the last 2–3 years. Second, we perform a data-driven analysis of the full disruption cycle containing three episodes, starting with the epidemic shock early in the year 2020, over the pandemic stalemate, towards the endemic-induced recovery in the year 2022. Third, we summarize the major insights and derive a set of policy recommendations and future research directions which we consider essential on the way towards what we call pandemic-resilient aviation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".