Industrial Concentration of the Brazilian Automobile Market and\n Positioning in the World Market
Bibliographic record
Abstract
This paper surveys the evolution of industrial concentration of the Brazilian\nautomotive market as well as its positioning in the worldmarket. Data available\nby OICA (International Organization of Motor Vehicle Manufacturers) were used\nto better understand the characteristics of the Brazilian market on the world\nstage. A cluster analysis algorithm (by the k-means technique) ranks Brazil\nwith a concentration profile in a group of countries like US and South Korea,\nin contrast to countries such as Germany, Canada and Japan, or even France and\nItaly. It is rather usual to characterize the market structure through\nindustrial concentration indices: we revisit CR ratios (concentration ratios),\nHHI (Herfindahl-Hirschman index), B (Rosenbluth index), and CCI (Horvath\ncomprehensive concentration index). Data of Anfavea-Brazil (Associacao Nacional\ndos Fabricantes de Veiculos Automotores) were used to estimate these indices in\nthe period 2012-2018 for the national automobile industry. The values obtained\nindicate that by 1998 the automotive sector was behaving as an\noligopoly-differentiated. However, the values of more recent periods\n(particularly CR4) strongly indicate that the sector is currently moderately\nconcentrated and is changing for a quasi-devolved market.\n
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".