Fundamentos en sistemas de evaluación de la educación superior: Colombia y Argentina
Bibliographic record
Abstract
A avaliação é, ao mesmo tempo, uma questão de urgência e complexa, devido à demanda da sociedade pela melhoria de suas instituições e organizações. O tema de pesquisa do presente trabalho pertence ao campo da avaliação da educação superior, o objeto de análises são os fundamentos conceituais da avaliação e sua presença na concepção dos Sistemas da Avaliação para a Educação Superior, levando em conta os casos de Brasil, Colômbia e Argentina. Este artigo pretende organizar um marco histórico referencial sobre a evolução dos fundamentos da avaliação, desde a avaliação da aprendizagem até a avaliação institucional; baseado nos aportes de Tyler, Crombach, Scriven, Guba y Lincoln, House, e Stufflebeam, e de autores europeus e latino americanos; para buscar a referência teórica das concepções que fundamentam os sistemas de avaliação. Finalmente são analisadas e comparadas as características das bases conceituais dos sistemas de avaliação do ensino superior acima mencionados.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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; both teacher heads agree on what is shown here.
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".