Re-Visioning Counsellor Education: Centring Justice, Accessibility, Inclusion, Diversity, and Equity
Bibliographic record
Abstract
In this chapter Sandra and Melissa focus on justice-doing within counsellor education. They highlight the necessity of learning environments and institutional cultures characterized by JAIDE (i.e., justice, accessibility , inclusion, diversity, and equity), in which all learners can thrive. They pair this with integration of cultural responsivity and social justice (CRSJ) practices into every facet of curricula and field placements to ensure that students are prepared to challenge systemic inequities and foster micro-, meso-, and macolevel change. They note the responsibility of educators and institutions to fully address the “Calls to Action” of the Truth and Reconciliation Commission of Canada. They organize the chapter into the following themes: (a) shifting from learning about to learning with and from, which includes embracing multiple ways of knowing, doing, and being; (b) implementing anti-oppressive and decolonial pedagogies to enhance the cultural meaningfulness of pedagogy; (c) creating safer, more inclusive learning spaces; (d) faculty development, which includes continued learning and unlearning through self-reflective processes; (e) program-level integration of CRSJ practices to ensure pluralism in views of health and healing are equitably reflected in all aspects of counsellor education; and (f) institutional advocacy to address access barriers and increase representation of racialized and other marginalized groups among faculty, staff, and learners. An invitation to action invites all members of the academic community to embrace the scholar–practitioner–advocate–leader model. Melissa and Sandra appreciate the insights about counsellor education offered by these co-authors: • Jane Arscott draws on her many years of experience of advocating for accessibility of education and recognition of learning derived from nonconventional and undervalued contexts and sources to re-vision education and learning. • Kim Ashbourne encourages counsellor educators to think beyond academic accommodation and embrace transformative digital accessibility in their teaching praxis. She suggests five high leverage changes educators can make. • Jagdeep Kundi reflects on her experience of mentorship as a student-led journey of learning and unlearning through which she was able to embrace decolonization and Indigenous ways of knowing. • Ya Xi (Nancy) Lei, Sherani Sivakumar, and Gina Wong speak to building a sense of safety in community as students and faculty engaged in the Asian Mental Health: Research, Advocacy, Working (AMH: RAW) group. • Ya Xi (Nancy) Lei offers insights into racialized students’ well-being through her research on critical incidents in racial (in)equity in Canadian counsellor education. • Marguerite Lengyell shares a series of videos that bring to life her counsellor education experiences, offering concrete strategies for navigating the complexities of CRSJ work in practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.054 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 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 teacher head, 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".