Decolonizing Policy Research as Restorative Research Justice: Applying an Indigenous Policy Research Framework (IPRF)
Bibliographic record
Abstract
What is required to decolonize policy research in doing knowledge production about Indigenous peoples? Policy studies has been complicit in maintaining a central methodological policy research problem: the ongoing prevalence of hegemonic imperial and colonial knowledge production practices in relation to Indigenous peoples. This problem persists through policy researchers producing anti-Indigenous genocidal native-place-invisibilization in scholarship. Ambiguous relationality is another mechanism through which elimination of the natives takes place in research – it is when researchers deliberately/unintentionally omit naming and visiblizing their positionality in relation to the native-places the researchers are working with. Undoing harms emerging from native-place-invisibilization and ambiguous relationality requires a ‘grounded normativity’ oriented native place consciousness, naming and visibilization of the native place(s) the researchers work on/with, respecting sovereign Indigenous research jurisdictions, and applying an Indigenous Policy Research Framework (IPRF). Decolonization as a solution to the policy problem being tackled in this paper looks like counter-hegemonic radical redistribution of power back to the community when conducting Indigenous policy research. The IPRF approach is formulated using a literature review methodology and consists of guiding questions and principles to help steward the processes of decolonizing policy research. The aim is to support the emergence of radically restorative research justice practices and repair historically harmful relations between knowledge-producing systems/institutions and the Indigenous communities about whom the knowledge production is done.
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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 | 0.183 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.011 | 0.114 |
| Scholarly communication | 0.029 | 0.032 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".