Quem está ficando para trás? Uma década de evasão nos cursos brasileiros de graduação em Administração de Empresas e Ciências Contábeis
Bibliographic record
Abstract
Dropout from public and private institutions of higher education (IHE) is associated with considerable social, academic and economic losses. These losses affect all of society, as citizens directly or indirectly pay for their own education and that of their relatives. The main purpose of this study was to analyze the dropout behavior of students enrolled in undergraduate programs of Business Administration and Accounting at Brazilian IHEs between 2001 and 2010. Dropout rates were investigated per type of IHE and correlated with the percentage of students completing their courses within the ideal time frame. Our hypotheses were tested with ANOVA and the variables were submitted to simple and multiple correspondence analysis. Descriptive statistics showed higher dropout levels for Business Administration than for Accounting, but lower overall levels when compared to the literature. The median ideal-term course completion index was higher for Accounting, though not significantly. Less than a quarter of the students completed their courses within the expected five-year period. In general, our findings indicate that Accounting programs have lower dropout rates in universities and university centers, whereas Business Administration programs have higher dropout rates in colleges, schools, institutes and technological education centers. The results of this study are not intended as a generalization, but represent patterns observed within the classifications adopted in the study.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".