Entrepreneurship Education in Socioeconomically Disadvantaged Contexts in Brazil
Bibliographic record
Abstract
This article discusses the impact of entrepreneurship education in socioeconomically disadvantaged contexts, emphasizing its perceived dynamic nature in reflecting personal values, social changes, and cultural differences (Lackéus, 2015; Loi et al., 2022; Berglund et al., 2020; Berglund & Johansson, 2007). The focus is on the implementation of the Empreende Jovem Fluminense (EJF) Program for high school students in poor communities in Rio de Janeiro. The EJF had the support of school directors, the State Department of Education, and a non-governmental organization serving children and adolescents at risk. A case study using content analysis was conducted to analyze speeches from school principals, coordinators, students, teachers, parents/guardians, and NGO representatives. The study found that the main contribution of education for entrepreneurship in disadvantaged contexts was the development of non-cognitive skills that can impact academic performance and job market outcomes. The program’s location on university premises also broadened the educational perspectives of the participants. Overall, the EJF is believed to be highly beneficial for the professional development of its participants.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".