Políticas de permanência na Universidade. Sucesso, perseverança e abandono: uma breve aproximação à questão no Quebec, Canadá.
Bibliographic record
Abstract
O artigo em apresentação busca desenvolver uma apreciação das políticas de permanência dos estudantes na universidade no contexto da América do Norte tendo o foco na província do Quebec, Canadá. O espelho da temática centra-se nos estudos sobre o sucesso, a perseverança e o abandono do ensino superior. O estudo produzido resulta de uma breve apreciação aos teóricos focados na discussão. Para fazer a coleta dos textos foi utilizado um “motor de pesquisa” que utiliza a base de dados ERIC. Um pouco mais específico foi consultado o “motor” Atrium da Université de Montréal. Para uma exploração mais genérica do objeto de estudo foi consultado o Google Scholar. Como o interesse da temática foca o Canadá e o Quebec uma quantidade de artigos e textos selecionados foi desconsiderada. Preservadas as proporções, os desafios no contexto observado prestam-se de referência para o estudo da problemática no Brasil, também. Embora as realidades sejam distintas as ações para potencializar a permanência e a perseverança presta-se comparativamente para combater o abandono do ensino superior.Palavras-chave: política educacional, ensino superior, permanência, sucesso.ABSTRACTThis article submission seeks to develop an appreciation for students policies of staying at the university in the context of North America with the focus in the province of Quebec, Canada. The mirror of the theme focuses on studies developped on success, perseverance and the abandonment of higher education. The study produced results from a brief examination focused on the theoretical discussion. To make the collection of texts one "search engine" that uses the ERIC data base. A bit more specific was found in the "engine" Atrium of the Université de Montréal. Google Scholar was consulted for a more general exploitation of the object of study. As the focused interest thematic was in Canada and Quebec a number of articles and selected texts was disregarded. Preserved the proportions noted challenges in the context of reference lend themselves to study the problem in Brazil, as well. Although the realities are different actions to enhance the permanence and perseverance lend themselves compared to combat the abandonment of higher education.Keywords: Educational policies; higher education, staying policies, success.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.034 | 0.019 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".