Airline Passenger Protections: More Data and Analysis Needed to Understand Effects of Flight Delays
Bibliographic record
Abstract
A letter report issued by the Government Accountability Office with an abstract that begins "Flight delays and cancellations are disruptive and costly for passengers, airlines, and the economy. Long tarmac delays have created hardships for some passengers. To enhance passenger protections in the event of flight disruptions, the U.S. Department of Transportation (DOT) recently introduced passenger protection regulations, including a rule that took effect in April 2010 designed to prevent tarmac delays more than 3 hours (the tarmac delay rule), as well as other efforts to improve passenger welfare. As requested, this report addresses (1) whether flight delays and cancellations differ by community size; (2) how DOT's tarmac delay rule has affected passengers and airlines; and (3) how passenger protection requirements in the United States, Canada, and the European Union (EU) affect passengers and airlines. GAO analyzed DOT data, including through the use of regression models, as well as data from FlightStats, a private source of flight performance information. GAO also reviewed documents and interviewed government, airline, and consumer group officials in the United States, Canada, and the EU."
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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".