Creating Space for Indigenous Research in Canadian Counselling Psychology Graduate Programs
Bibliographic record
Abstract
This article builds off the symposium presentation entitled “Decolonizing Canadian Counselling Psychology: Creating Space for Indigenous Scholarship” which was delivered by the authors at the 2018 Canadian Counselling Psychology Conference. The symposium presented Ms. Inkster and Ms. Smeja’s respective Master’s research projects, while Dr. Jordan and Dr. Goodwill shared their supervisory experiences overseeing research aimed at advocating for Indigenous communities. This paper expands on the individual presentation topics by discussing broader systemic issues and considerations relevant to making space for Indigenous scholarship within Canadian CP programs. Personal narratives are weaved throughout the paper, emphasizing challenges in academic environments, resilience and resistance strategies, as well as the important role of mentors in graduate students’ decision to pursue Indigenous Research Methods. Specific recommendations addressed to our field are also discussed.
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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 | 0.045 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.083 | 0.041 |
| Scholarly communication | 0.023 | 0.009 |
| Open science | 0.008 | 0.063 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 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".