Bibliographic record
Abstract
This research project investigates the impact of uncontrollable factors on companies, which can include natural disasters, political instability, and global pandemics. This paper focuses on the impact of an air crash on airline companies, this uncontrollable factor has significant consequences for the financial, operational, and reputational aspects of airlines, and understanding the impact is crucial for developing effective strategies to mitigate them. The study utilizes a mixed-methods approach, combining quantitative analysis of airline data with a special case study of the air crash that happened on the Eastern Airline, to provide a comprehensive analysis of the topic. The research findings reveal that uncontrollable factors can lead to the loss of reputation, reduced customer demand, and facing operational disruptions. The public's perception of the airline's safety record will be negatively impacted, which can result in a loss of customer trust and loyalty. Following an air crash, people may be hesitant to fly with the airline, resulting in a reduction in demand for their services. The significance of this research lies in its contribution to the understanding of the impact of uncontrollable factors on airlines, especially the impact of an air crash on an airline company. Even in an air crash due to factors beyond human control, people still try to avoid flying or flying with the same company for a period of time after the accident.
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".