Reforço escolar: análise comparada dos meandros de um fenômeno em crescimento
Bibliographic record
Abstract
Based on data from the Project “Xplika International: comparative analysis of the private tutoring market in five capital cities”, we analyze the phenomenon of private supplementary tutoring in four cities: Brasilia, Lisbon, Seoul and Ottawa. Our theoretical framework is the comparative sociopolitical analysis of education and we focus our piece of research on three areas: reasons for students to attend this type of educational support, most in-demand subjects and time weekly spent in tutoring. The research methodology is based on interviews and questionnaires that were applied, respectively, to managers and students of private tutoring companies in four cities. The time devoted to this activity, the subjects most sought and the belief in its contribution to the academic success allow us to build an informative picture of the phenomenon of private supplementary tutoring in the four cities. Among the key findings, private tutoring is a fast growing industry on a global scale with a multitude of businesses that claim the role of drivers for reinforcing the learning of formal education, but whose political, economic, psycho-pedagogical and educational consequences require thorough analysis. We are facing an education market that challenges formal schooling, equity and success in education. That’s why we need to engage in thorough research, in order to shed more light on the shadows of this phenomenon.Key words: private tutoring, comparative education, educational market.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".