PESQUISA SOBRE E NAS FRONTEIRAS : ENTREVISTA COM O DR. CRISTHIAN TEÓFILO DA SILVA
Bibliographic record
Abstract
Antropólogo na Secretaria de Articulação e Promoção dos Direitos Indígenas (SEART), do Ministério dos Povos Indígenas (MPI), e Professor Associado 4 do Departamento de Estudos Latino-Americanos (ELA) do Instituto de Ciências Sociais (ICS) da Universidade de Brasília (UnB). Bolsista de Produtividade em Pesquisa do CNPq – Nível 2. Sócio efetivo da Associação Brasileira de Antropologia (ABA) e da Sociedade Canadense de Antropologia (CASCA). Doutor e Mestre em Antropologia e Bacharel em Ciências Sociais com Habilitação em Antropologia pela UnB. Realizou pós-doutorados no Centro Interuniversitário de Estudos e Pesquisas Indígenas (CIÉRA) da Université Laval, onde é Pesquisador Associado, e no Centro de Pesquisa e Pós-Graduação sobre as Américas (CEPPAC) da UnB. Integra como pesquisador o Programa Identidade Cultural e Direitos Humanos do Instituto de Investigação em Direito da Universidad Autónoma de Chile (UAC), atuando na linha de pesquisa Constitucionalismo em Rede. Fundador e coordenador do Laboratório de Estudos e Pesquisas Colaborativas com Povos Indígenas, Comunidades dos Quilombos e Povos e Comunidades Tradicionais (LAEPI – Grupo de Pesquisa do CNPq).
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".