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Record W7135181473 · doi:10.71446/ek5793779

Dismantling harmful legacies of counsellor education in Canada: A new era of lionhearted practices

2025· book-chapter· W7135181473 on OpenAlexaboutno aff
Gina Wong, Sherani Sivakumar, Ya Xi (Nancy) Lei, Yevgen Yasynskyy

Bibliographic record

Venuenot available
Typebook-chapter
Language
FieldSocial Sciences
TopicCritical Race Theory in Education
Canadian institutionsnot available
Fundersnot available
KeywordsHarmCurriculumRacismFeelingBest practiceGrading (engineering)Lived experience

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score0.758

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0570.022
Scholarly communication0.0140.005
Open science0.0030.010
Research integrity0.0040.010
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.029
GPT teacher head0.361
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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