Gestão do desenvolvimento: a atuação da Secretaria de Estado de Indústria, Comércio e Energia e suas repercussões no mercado de trabalho maranhense
Bibliographic record
Abstract
ABSTRACT The present study has as main objective to analyze the performance of the Secretaria de Estado de Industria, Comercio e Energia do Maranhao in processes to incentivize economic activity and its repercussions in the Maranhao labor market. Based on the database of the Instituto Brasileiro de Geografia e Estatistica (IBGE), Relacao Annual de Informacoes Sociais (RAIS), Pesquisa Nacional por Amostra de Domicilios Continua (PNAD Continua), Cadastro Geral de Empregados e Desempregados (CAGED), among others data. The survey covered the entire state of Maranhao since 2014. The analysis of the results was based on descriptive statistics, using graphical and tabular methods. Regarding the results of the survey, it was verified that the unemployment rate in the State of Maranhao has increased in the last years even with investments in the economy, from 6.4% in the first quarter of 2014 to 13.7% in the 3rd quarter of 2018, the current result is 0.7% lower than the result of the Northeast and 1.8% higher than the result of Brazil. The sectors that are the responsibility of SEINC had regression in the distribution of employed people, the industrial sector, for example, had a decrease of 0.8% in the third quarter of 2018 in relation to the same period of 2017 and the commerce regressed 0.1 %.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".