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O programa Mulheres Mil como política pública de educação profissional: levantamento e descritores das produções acadêmicas em nível stricto sensu (2013-2021)

2022· article· en· W4297445782 on OpenAlexaboutno aff
Márcio Adriano de Azevedo, Tathyane Torres da Silva Duarte, Sandyeva Francione Silva Araújo

Bibliographic record

VenueVértices · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Public Policy
Canadian institutionsnot available
Fundersnot available
KeywordsLibrary scienceVulnerability (computing)Political scienceSociologyHumanitiesArt

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.045
GPT teacher head0.415
Teacher spread0.370 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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