Entrevista: Maria Cristiane Barbosa Galvão
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Maria Cristiane Barbosa é professora do Departamento de Medicina Social da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo. Doutora em Ciência da Informação pela Universidade de Brasília, realizou estágio na Universidade de Montreal. Além disso, foi pesquisadora associada da Universidade de Campinas e professora visitante do Departamento de Medicina de Família da Faculdade de Medicina da Universidade McGill e da Universidade de Málaga. Pesquisadora na área de informação em saúde, nesta entrevista, traz reflexões sobre a importância de pesquisadores e profissionais da informação na área da saúde no contexto da pandemia de Covid-19. A entrevista foi realizada em outubro de 2020.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 it