(De) Colonizing Research?: Biopower, Whiteness, and the Stranger in Research Ethics Protocol Involving Aboriginal Peoples
Bibliographic record
Abstract
Given recent release of Tri-Council Policy Statement 2 (TCPS 2; CIHR, NSERC, and SSHRC 2010), it is timely to review its implications as national ethical guidelines for all academic research in Canada. The section I focus on is specifically on chapter that guides research involving Indigenous peoples. I engage in a critical race analysis of how race is deployed in this federal policy on research ethics. Informed by Foucault (2003), Moreton- Robinson (2006), and Ahmed (2000), I begin by establishing this conceptual framework, which uses biopower, whiteness, and the stranger. Through exploring how these concepts may work together, I offer a conceptual framework to examine race in TCPS2. Next, I discuss TCPS2 chapter on Indigenous peoples in research, followed by my analysis of this section. I look to answer question: what deployments of race are exposed when analyzing TCPS2 using biopower, whiteness, and stranger? To conclude, I look to expose these deployments of race through this unique analytic framework. I hope to contribute to existing literature, thinking, and criticism on how race works within research protocol that focuses on Indigenous peoples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.181 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.039 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".