Bibliographic record
Abstract
Este artigo oferece uma perspectiva crítica e retrospectiva sobre as pesquisas apresentadas número especial da revista Educativa intitulado “Avaliação das aprendizagens para sustentar e promover a diversidade” (Barroso da Costa; Araújo-Oliveira, 2021), com o objetivo de responder de maneira abrangente as grandes questões levantadas na introdução do mesmo e, assim, recontextualizar a questão da avaliação da aprendizagem e a necessidade de inovar para melhor apoiar a diversidade em todos os discentes, conforme sugerido pelo título deste número. Uma análise transversal dessas pesquisas, tendo em mente as orientações fornecidas aos autores, permite, por um lado, destacar os principais resultados e conclusões apresentadas pelos estudos e, por outro lado, identificar diversos desafios que surgem das pesquisas analisadas, os quais oferecem muitas sugestões para o empreendimento de futuras pesquisas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".