Radical Critical Policy Studies: Situating Racialized Personhood within Decolonizing Policy, Knowledge Production, Self-Reflexivity & Positionality/Social-Location
Bibliographic record
Abstract
This article addresses weaknesses in critical policy studies (CPS), by proposing we move toward adopting radical critical policy studies (RCPS) theory and empirical methodological approaches. This moves away from the euro-centric colonized/colonizer dichotomy, decentering whiteness/white gaze and situates racialized personhood at the center of decolonizing knowledge production. RCPS’ intervention forces racialized academics/researchers to be cognizant of how although we navigate our own oppression/colonization within colonial institutions (academia, the state) we are not immune to reproducing this same system of harm we wish to dismantle by using Colonial Logics (the colonizers tool of whiteness, performative colonialism, systemic oppression and harmful practices) and engaging in Colonial by Proxy (see Diagram 5). We expand on RCPS with the concept of Decolonizing Self-Reflexivity and Positionality/Social-Location (Diagram 3, Decolonizing Self-Reflexivity and Positionality & Social-Location Checklist tool) with the aim to hold ourselves accountable to higher standards, to be more critically self-aware of the complex duality of our privileged social-location/positionality, while de-colonizing our self-reflexivity with an active duty to prevent the reproduction of systemic performative colonialism throughout policy-making (as policy-makers) or in academia.
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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 | 0.041 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.083 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".