Bibliographic record
Abstract
Airbus and Boeing are already preparing to shape the future of air transport with their latest airliners. However, when passengers unluckily encounter disruption in their air travel, they still need to use the traditional, cost-ineffective method to seek redress for their claims, causing a psychological gap between the next-generation passenger jet and the old way of dealing with their travelling issues. The post-disruption complaint-handling system can step forward to fit the future and current demands and characteristics; a comprehensive airline passenger Alternative Dispute Resolution (ADR) framework can be a helper in shaping the future experience in aviation dispute resolution. Such a comprehensive framework could serve various types of passengers’ claims from small to major under the Montreal Convention or Regulation (EC) No. 261/ 2004, and the framework also has a quality augmentation mechanism (QAM) to enhance its quality, overcome its potential pitfalls and concerns, enable the framework to operate sustainably and promote the positive development of air passenger dispute resolution and the air transport sector. The QAM-enhanced comprehensive aviation ADR framework can, together with innovative air transport, renovate the air transport service from ‘nose to tail’.
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 | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".