Time Out at AirTran: the Low-Cost Carrier is Getting Used to Life After Double-Digit Growth
Bibliographic record
Abstract
This article looks at the current state of AirTran, a low-cost carrier (LCC) based in Orlando, Florida, that experienced double-digit growth in seat-miles in the middle part of the first decade of this century. But last year, with the increase in the price of oil, capacity grew just 4.9 percent, with capacity falling 6.9 percent in the final quarter of 2008 compared to 2007. The fuel cost increase was especially hard on AirTran despite having a young, relatively fuel-efficient fleet because of its route structure. It is nearly exclusively domestic, where it is currently difficult to make money. AirTran reported a $273.8-million net loss for 2008, its first losing year since 1999. It had sales of $2.41 billion. AirTran is still cost-competitive, with costs of about six cents a seat-mile, and it doesn’t worry about losing market share to competitors because they are retrenching, too. Its main focus is building up its cash reserves, which are now at $340.5 million. The goal is to achieve $600 million. According to the airline's CEO, the recession will make customers appreciate AirTran’s value pricing even more, though it has already eliminated 47 aircraft from its fleet plan for 2009-2012, disposing of them before the market for them shrank. After the failure of a takeover bid in 2006-2007, AirTran is keeping its route structure essentially unchanged. Charts show market shares in selected markets and selected results.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.014 |
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".