Technological and educational challenges towards pandemic-resilient aviation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While COVID-19 has devastating effects on aviation, several recent studies have highlighted the potential of the pandemic-induced break for rethinking air transportation, hopefully orchestrating changes towards the construction of a more pandemic-resilient aviation system. Here, pandemic-resilient means that aviation stakeholders can sustain the impact of an epidemic or pandemic outbreak through a more informed reallocation of their resources and more collaborative decision making, while being able to minimize the impacts of external events. Our study contributes to the literature by discussing the challenges associated with technological innovation and education of aviation professionals, on the way towards pandemic-resilient aviation. We discuss issues surrounding technologies for smarter aircraft, smarter airports, and smarter airlines. While technology ensures long-term competitiveness and sustainability, an often-ignored source of challenges are human resources and education. COVID-19 has uncovered and magnified the effects of severe concerns with the current aviation education system, which need to be solved by extended skill sets, modern technology, and better career perspectives. Without properly addressing these technological and educational challenges, the aviation industry likely misses an distinct opportunity for restructuring towards pandemic-resilient aviation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 it