Leashes and Lies: Navigating the Colonial Tensions of Institutional Ethics of Research Involving Indigenous Peoples in Canada
Bibliographic record
Abstract
Ethical standards of conduct in research undertaken at Canadian universities involving humans has been guided by the three federal research funding agencies through the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (or TCPS for short) since 1998. The statement was revised for the first time in 2010 and is now commonly referred to as the TCPS2, which includes an entire chapter (Chapter 9) devoted to the subject of research involving First Nations, Inuit, and Métis peoples of Canada. While the establishment of TCPS2 is an important initial step on the long road towards decolonizing Indigenous research within the academy, our frustrations—which echo those of many colleagues struggling to do research “in a good way” (see, for example, Ball & Janyst 2008; Bull, 2008; Guta et al., 2010) within this framework—highlight the urgent work that remains to be done if university-based researchers are to be enabled by establishment channels to do “ethical” research with Aboriginal peoples. In our (and others’) experience to date, we seem to have been able to do research in a good way, despite, not because of the TCPS2 (see Castleden et al., 2012). The disconnect between the stated goals of TCPS2, and the challenges researchers face when attempting to navigate how individual, rotating members of REBs interpret the TPCS2 and operate within this framework, begs the question: Wherein lies the disconnect? A number of scholars are currently researching this divide (see for example see Guta et al. 2010; Flicker & Worthington, 2011; and Guta et al., 2013). In this editorial, we offer an anecdote to illustrate our experience regarding some of these tensions and then offer reflections about what might need to change for the next iteration of the TCPS.
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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 | 0.042 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.094 | 0.081 |
| Scholarly communication | 0.031 | 0.009 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".