Bibliographic record
Abstract
This thought piece provides helpful information about ethical research practices related to research involving Indigenous peoples so that academic librarians (both Indigenous and non-Indigenous) are better informed about the complex issues that exist and arise in such endeavours. Woven throughout the paper are guidance and strategies to avoid causing harm when doing research with Indigenous peoples and communities, such as misrepresenting Indigenous peoples, cultures, and epistemologies. A brief account of the legacy of a long history of unethical research practices conducted by Western researchers who extracted Indigenous knowledge speaks to why Indigenous peoples do not trust academic research projects. Researchers need to question their own motives when they consider conducting research with Indigenous peoples and to respect that we want to be involved in our own solutions and in research that utilizes Indigenous values, with the goal that “nothing [is done] about us without us.” Key to building relationships and finding success in the research undertaken are an in-depth understanding of Indigenous protocols, values, and ways of knowing, as well as evidence of the researcher making a long-term commitment to the research and the community. Further, such an understanding provides an access point for librarians to contribute to the decolonization of library services while supporting Indigenous researchers.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.046 | 0.011 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".