Dismantling harmful legacies of counsellor education in Canada: A new era of lionhearted practices
Bibliographic record
Abstract
This chapter is written from the perspectives of three racialized Asian women with lived experiences of navigating colonial and oppressive practices in academia within the landscape of counsellor education. The need for lionhearted practices is rooted in the lived reality that many racialized individuals experience harm and trauma from racial discrimination permeating the educational landscape, ranging from in-class interactions, curriculum materials, to grading criteria. The damage from continual exposure to these practices contributes to feelings of inadequate safety and support, leading many to consider leaving counsellor education altogether. To enhance safety, counsellor education must embed tools that support students and instructors in integrating steps towards greater cultural safety. Learning to facilitate lionhearted conversations, which are courageous dialogues that challenge biases and address harm directly, creates a more inclusive space for students. This practice is also crucial for promoting more responsive care for counselling clients who do not fit the conventional mold that counsellor education has historically prepared students for, an urgent endeavor given that racism is a public health emergency. In this chapter, Gina, Sherani, and Ya Xi collaboratively present the lived experiences that ground their work, a framework for understanding lionhearted conversations, and a self-assessment matrix wheel designed to help readers consider the lionhearted stances they can take when engaging in this critical work. Drawing on Yevgen’s instructional design expertise, they offer a series of animated videos to encourage applied practice of lionhearted stances. The chapter concludes with a call for systemic action to address harmful legacies in counsellor education, examines the impact on the well-being of those harmed, and invites genuine allyship to foster a culture of lionhearted engagement across the counselling profession.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.057 | 0.022 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".