INTRODUCING THE LOW COST-LOW FARE CONCEPT IN BRAZIL: GOL AIRLINES
Bibliographic record
Abstract
This paper presents the introduction of a low-cost/low-fare (LC/LF) concept in Brazil by Gol Airlines, beginning from January 2001. We show that some of the core LC/LF strategies of operations and management decisions put into action by North American and European carriers like Southwest and Jetblue, and Ryanair and easyjet, respectively, could be implemented by Gol in the beginning of its operations, while several others still cannot be implemented at all. Moreover, the paper analyzes the impacts and contributions that have resulted from the introduction of Gol Airlines and its Brazilian LC/LLF concept, while presenting and discussing the main issues within the crisis involving the major airlines of Brazil. The recently announced possible future merger between the two largest Brazilian carriers, VARIG and TAM, is addressed as a major adversary to the growth of Gol's operations and its desired expansion in the Brazilian air transport market. While discussing some of the possible outcomes of this merger to the Brazilian domestic market, we present some of the strategies already being implemented by the LC/LF carrier to counter this threat, plus another major step towards Gol's future growth: the go-ahead of the Brazilian government on the injection of capital from AIG, to buy 20% of the airline's shares in the first quarter of 2003.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".