Riquezas nas diferenças na cibercultura: em prol de práticas educativas mais democráticas e inovadoras
Bibliographic record
Abstract
Neste artigo, apresentamos cenários que ilustram a riqueza das diferenças na cibercultura em direção a práticas educacionais mais democráticas e inovadoras. Trata-se de uma pesquisa qualitativa que segue uma metodologia pós-crítica. Consideramos alunos e professores de universidades brasileiras e canadenses com foco na compreensão da cultura digital e suas implicações no processo educacional. Usamos o Facebook e o WhatsApp para coletar narrativas. Os resultados apontam que o uso de espaços digitais (in)forma professores e alunos sobre outras práticas educacionais que os permitem outras formas de expressões de identidade, valorizando e ouvindo outras vozes que são silenciadas (por exemplo, indígenas, 2SLGBTQ+ e pessoas racializadas).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".