Indigenous Voices in the Knowledge Production
Bibliographic record
Abstract
In Brazil, in recent years, we have observed an increase in the access of indigenous people in Higher Education and Postgraduate Studies. They demand recognition as producers of scientific knowledge, overcoming the colonial paradigms that objectify them in the production of knowledge. However, in the case of Public Health, persists a very low visibility of indigenous participation in academia. This experience report presents the project “Indigenous Voices in the Production of Knowledge”, carried out through a partnership between a collective of indigenous researchers from different regions of the country and researchers from “Academic Institution”. A network of 20 indigenous researchers was organized, who met in 2019 and highlighted the need to “give recognition to indigenous knowledge” as a scientific expression; the difficulties of mastering the Portuguese language; the collective character of indigenous authorship and the need to make indigenous identities visible in authorship in scientific productions. An Indigenous Editorial Board was formed and prepared a public call for indigenous authors, which resulted in an open access publication with 21 texts. The editorial process sought to qualify the editorial board in the editorial production process; indigenous researchers carried out the peer review of the submitted texts; and virtual meetings were held with the authors to discuss the opinions. In addition to a quality academic production by indigenous authors, the project strengthened the network of indigenous researchers in collective health area, involving them in other events and initiatives, and triggered institutional changes at “Academic Institution”.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.022 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".