Indigenous sovereignty in research and epistemic justice: Truth telling through research
Bibliographic record
Abstract
in which Indigenous knowledge systems are recognised and valued in research-related contexts. We draw attention to how colonial knowledge systems silence, delegitimise and devalue specific knowers and ways of knowing, being and doing - through truth telling. This includes (1) the extent to which educational systems, research, practices, decisions, and reported outcomes are whitewashed - a process of structural and systemic discrimination, racism, and exclusion that actively alters or omits Indigenous and non-Euro-Western contributions and perspectives to fit Euro-Western norms and (2) whitewashed and racialised logic in scientific research that claims to be open, collaborative and transparent. Whitewashing not only obscures the history and contributions of Indigenous peoples and communities but also actively reinforces systemic biases and inequities. We assert the need for epistemic justice in public health research. Epistemic justice calls for Indigenous sovereignty and self-determination to be made visible. It may involve on how colonial policies, protocols, and regulations are connected to everyday lived inequities of Indigenous communities, families and individuals. Ultimately, epistemic justice is inherent to Indigenous peoples' health and wellness, self-determination and sovereignty.
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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 | 0.236 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.020 | 0.203 |
| Scholarly communication | 0.033 | 0.039 |
| Open science | 0.004 | 0.035 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".