EMBRAER - Empresa Brasileira de Aeron�utica S.A. (Brazilian aerospace conglomerate): Brazilian airc
Bibliographic record
Abstract
In 2017, Embraer held 58 percent of the world's market share in commercial jets for the regional aviation market.In addition, according to projections, Embraer will have to produce and deliver a commercial jet every two days over a twenty-year period.According to the International Civil Aviation Organization, there are 1,548 aircraft manufacturing companies in the world.However, only a few can be considered "assemblies" and only five of these carry out large-scale production: Airbus (Europe), Boeing (USA), Bombardier (Canada), Embraer (Brazil) and Tupolev (Russia).Embraer emerged in a 'developing country' with no tradition in the aerospace sector and became a strong competitor in the small and medium-sized global aircraft market.To understand Embraer's experience, a synthetic review was carried out of the main theories related to the internationalization process.Moreover, primary documents were used in this study, as well as extensive literature on the company's history and internationalization process.The starting point is the hypothesis that internationalization had a fundamental impact on the growth of the company.As a preliminary conclusion, it is possible to say that Embraer started out with the "world market" in mind.Likewise, it is possible to affirm that the decision to focus on executive, military, small and medium-sized aircraft aviation to boost regional markets proved to be assertive.
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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 | 0.003 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.009 |
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".