Teaching Teachers for Brazilian Universities: A Content Analysis of Scientific Publications Focused on the Sustainability of Stricto Sensu Programs
Bibliographic record
Abstract
After more than four decades of post-graduate programs in Brazil focused on the preparation of teachers – the Stricto and Lato Sensu programs – we have verified a gearing frequency of academic works that reflect on these programs. Through a bibliometric analysis and a content analysis of scientific publications focused on the Stricto Sensu programs we conclude that the current stage of research development shows that these programs are associated with a concentrated focus on research and that pedagogical preparation is being made secondary. The methodology used was qualitative, data from the Brazilian Platform for Higher Education Personnel Improvement Coordination (CAPES) and the Scientific Electronic Library Online (Scielo) were used and analyzed using the Iramuteq software.The results showed that since the 2000s the number of doctoral theses and master's dissertations has grown and the area with the most publications refers to the health sciences.This bias motivates an accessory debate as well as revealing the implications of the focus of these programs.
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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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.033 | 0.050 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".