Falling short of university admission in Brazil: responses from <i>cursinhos populares</i> social movements
Bibliographic record
Abstract
Cursinhos populares are grassroots experiences organized to overcome the small proportion of marginalized groups in prestigious universities, and an example of activism against inequality in Brazilian higher education. They constitute a critique of hegemonic models promoted by the market as well as from the state and thus can be considered an example of commons education: by their prefigurative politics, they build curricula and activism towards the reshaping of university admission. They contribute to the building of a commons perspective rooted in Latin-American social movements tradition. Drawing from our experience as educators in Rede Emancipa, we discuss the question ‘why does a group of students, mostly Black young people from peripheric territories, fall short of public universities admission in Brazil?’. We review possible answers both from the hegemonic and the alternative point of view. We problematize what is conceived as ‘quality education’ expressed in the standardization of curricular time and teaching material, considering political and pedagogical aspects. Finally, we report two experiences developed within Rede Emancipa - ‘Emancipa circles’ and ‘free time’. These experiences reflect on the use of time in a cursinho popular, exercise the integration of knowledge, and stimulate taking a stand by all the subjects of this movement.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".