Povos Indígenas, processos colaborativos e repatriação virtual: notas sobre a Coleção Carlos Estevão de Oliveira do Museu do Estado de Pernambuco – entrevista com o professor dr. Renato Athias – UFPE
Bibliographic record
Abstract
Os temas da colaboração entre povos indígenas e os museus e da repatriação de objetos de coleções etnográficas para os seus lugares de origem têm estado nas agendas dos antropólogos e museólogos nas últimas décadas (AUGUSTAT, 2017; ROCA, 2015). Nos Estados Unidos da América, por exemplo, existe em andamento uma lei que visa à repatriação de objetos indígenas para suas comunidades. Esta lei, intitulada Native American Graves Protection and Repatriation Act (NAGPRA), exige que os museus identifiquem e, dependendo do caso, retornem todos os restos humanos e os artefatos relacionados aos ritos funerários dos povos indígenas dos EUA. Experiência semelhante também é possível de se observar em Vancouver, no Canadá, especialmente no Museum of Anthropology (MoA) ligado à University of British Columbia (UBC), o qual assumiu o compromisso de trazer para a instituição povos indígenas canadenses para colaborar na confecção de narrativas expográficas com objetos originários de suas culturas. São sobre estes temas, centrais para antropologia e a museologia na atualidade, que trata esta entrevista com o Prof. Renato Athias.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.023 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".