Bibliographic record
Abstract
When a Jet Blue airliner with jammed front wheels made a dramatic landing at Los Angeles International airport in September, what the passengers and public didn't know was that Jet Blue has outsourced its heavy maintenance to El Salvador and Canada. Flyer Beware examines the safety and security consequences of the airline industry's increasing reliance on outsourcing airline maintenance. We know that more than half of the 4.9 billion dollars the airlines spend on maintenance is outsourced. Contracting this work has become a common practice in recent years, as major carriers cope with bankruptcies, sharp competition and rising fuel costs. Some carriers in the industry are contracting out more work than others. Frontier, Delta and American outsource less than the other airlines surveyed by the Inspector General of the Department of Transportation (contracting out 33 percent, 35 percent and 42 percent respectively), while America West and Alaska outsource the most in this group (72 percent and 80 percent). According to the Bureau of Transportation Statistics, the major carriers have shed over 100,000 airline jobs in the past five years. It's bad enough that these workers are losing their jobs, but what do these numbers really mean to the traveling public?
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.571 | 0.270 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".