Effects of Unemployment on Economic Sectors: A Proposal for Behavior Analysis with Brazilian Municipalities
Bibliographic record
Abstract
Although determined municipal public policies focus on the unemployment rate, to understand its determinants, one must assess how the main employment sectors in the country work, and how they affected by unemployment. In view of these facts, it is possible to raise the following research question: what is the influence of the main employment sectors on the unemployment present in Brazilian municipalities? Thus, this research will aim to analyze the effects of unemployment in the main employment sectors in Brazil. The study used data from 5.631 Brazilian municipalities, performing quantitative descriptive analysis procedures, in addition to the development of a linear regression model and a Tobit regression model. The Services sector appears with a positive highlight in relation to the others. Mineral Extractivism, on the other hand, presented a worrying unemployment estimate, being the sector that suffers the greatest impact in relation to unemployment. Among the economic sectors that also presented worrying coefficients are the Transformation industry (0.80) and Business (0.77). The results presented in the research can serve as a basis for decision-making, not only for public sector managers, but also for managers in the private sector.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".