Knowledge Economy in Brazil: Analysis of Sectoral Concentration and Production by Region
Bibliographic record
Abstract
The research presented in this article investigates and analyzes the concentration of knowledge production in Brazil, in the context of a public policy, at postgraduate level, by using the spectral methods grounded on the LQ (location quotient) and CI (concentration index) indicators, in three dimensions, from 2013 to 2018. The dimensions are economics, geography, and time. Economics is represented by Fields and Major Fields of knowledge production. Geography corresponds to the regions identified by each Federation unit (FU). Time is a chronological unit of the timeline in which knowledge is produced. The research then evaluates knowledge concentration in the income performance of the families by FU. The results are robust and indicate significant evidence that sectorial knowledge production in Brazil is regionally unequal and impacts on family incomes, but those family incomes evolve regardless of the knowledge concentration level produced. The research contributions are relevant to assist public policy regulators and monitoring managers, as well as to encourage future discoveries in regional economics applications.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".