O programa Mulheres Mil como política pública de educação profissional: levantamento e descritores das produções acadêmicas em nível stricto sensu (2013-2021)
Bibliographic record
Abstract
The article aims to collect the academic production about the Mulheres Mil Program, based on scientific academic productions at the stricto sensu level, as part of ongoing qualitative research by the Graduate Program in Professional Education of the Federal Institute of Education, Science and Technology of Rio Grande do Norte, following the theoretical-methodological procedures of bibliographic and documentary analysis, based on the CAPES’ Database of Theses and Dissertations (2019-2021). As a professional education public policy, the Program was initially conceived and implemented as a pilot project in thirteen states in the North and Northeast regions of Brazil, through an agreement established with Canada, starting in 2007. Initially, the objective would be promoting professional and technical qualification for 1,000 women from the North and Northeast regions of the country, until the year 2010. In 2011, a MEC Ordinance institutionalized the Program at a national level, aiming to qualify 100,000 women in social vulnerability, until 2014. The partial results show that research on the Mulheres Mil is still incipient to indicate the results of its implementation and the achievement of the objectives regarding the original design.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".