Take off and Crash: Lessons from the Diverging Fates of the Brazilian and Argentine Aircraft Industries
Bibliographic record
Abstract
What are the factors that allow for success or failure of developing countries' attempts to enter high-tech sectors? We make a initial attempt to answer that question through a comparative study of success and failure in manufacturing aircraft. Aircraft production is one of the key industries in the world today, as reflected in the intense Boeing-Airbus rivalry. It is also one of the most cyclical, technologically-sophisticated, and capital-intensive industries, and therefore an unlikely place for a developing country to compete. But almost from the birth of modern commercial aircraft manufacturing, Argentina's Fábrica Militar de Aviones (FMA) was at the forefront of production. Brazil's aircraft industry was tiny in comparison at that time. Yet, by the 1990s, Brazil's Embraer had become the world's third largest aircraft manufacturer, while the Argentine aircraft industry has virtually disappeared. We examine the history of each company to explain the differences in trajectories and their fates. Our analysis demonstrates that an evolutionary but consistent partnership between state and firm, one attuned to both the exigencies of sectoral development and to changes in the nature of global markets, is necessary for success.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".