Bibliographic record
Abstract
A pianista Stefanie Freitas foi a entrevistada desta edição. A pernambucana radicada em Porto Alegre fala de sua iniciação musical lúdica, comenta suas preferências de repertório que remetem à aquisição de seu gosto pela música através da escuta ainda na infância e apresenta peças de Schumann, Beethoven, Debussy e Brahms em gravações ao vivo feitas em seus recitais no Instituto de Artes da UFRGS. \n \nStefanie Freitas é formada pelo Conservatório Pernambucano de Música, Bacharel em Música pela Universidade Federal de Pernambuco, Mestre em Práticas Interpretativas pela UFRGS e atualmente está cursando Doutorado nesta mesma universidade, sob a orientação da professora Cristina Capparelli. Stefanie teve aulas com renomados pianistas, como Edson Bandeira de Mello, José Alberto Kaplan, Marlos Nobre e Alexandre Dossin. Ao longo de sua trajetória recebeu diversos prêmios, tanto como camerista quanto como solista.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.012 |
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".