Bibliographic record
Abstract
Over the past 40 years, global air travel has increased eight-fold: In 1974 air planes carried 421 million people globally. This means that global air travel has been growing up about 5% every year for 4 decades and this trend is expected to continue in the future. While demand growth is an important factor for the profitability of the airline industry, its impact quite depends on the operational performances such as load factor, passenger yield, labor efficiency and fuel efficiency. So, the objective of this study is to analyze companies competing in the airline industry to address how to use the return on invested capital (ROIC) tree model to analyze the effect of operational performances on airline companies’ financial performance and then how to increase the financially inferior company’s per-formance by intimidating the operationally and financially superior and productive company. In the case of the Korean airline industry, two leading legacy airline companies called as a company A and B were selected to do the computational study for the effect of operational performance on the financial performance and productivity. We analyzed the financially high-performing company using the ROIC tree model and then looked at financially how much the inferior company would be improved if it could imitate some factor consisting of the productivity ratios from the financially high-performing company.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".