Indigenous Rights in Research: Future Needs for Involvement of Indigenous Perspectives
Bibliographic record
Abstract
(Watch the RECORDING.) Over the last decades, there has been a movement towards higher degree of recognition and involvement of Indigenous knowledge and perspectives worldwide. An integral and defining part of this development is the work of scholars (and activists) from all over the Indigenous worlds claiming the necessity of decolonization/Indigenization and the recognition of Indigenous rights. Today, this struggle is not over. But change has happened. Indigenous research is a growing field. The ethics and formalities of publication and data management are addressed in ways that aim to answer to the needs of Indigenous communities. This panel takes the current situation and diversity as a starting point and looks to the future needs and challenges, and asks how can we continue working for research practices that are FAIR and include CARE? How can we learn across different Indigenous contexts and areas? The panelists represent research communities from different parts of the global north with its variety of Indigenous cultures.
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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 | 0.172 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.039 |
| Scholarly communication | 0.026 | 0.053 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.024 | 0.034 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".