Learning from the Standpoints of Minoritized Students: An Exploration of Multicultural and Social Justice Counseling Training
Bibliographic record
Abstract
The results of a feminist research endeavour that explored multicultural (MC) counselling and social justice (SJ) training experiences from the standpoint of eight culturally non-dominant doctoral students are presented. Participants represented students within the five counselling psychology programs accredited by the Canadian Psychological Association. Specifically, the research aimed to address the following research question: How do counselling psychology doctoral students who self-identify with non-dominant cultural identities perceive their experiences of MC and SJ training? This research adopted a feminist standpoint theory epistemology to guide an interpretative phenomenological analysis to reflect the culturally rich, complex, and situated experiences of participants, while concurrently emphasizing the role that systems of privilege and oppression play in influencing these experiences. Results point to seven superordinate themes, including: (a) MC and SJ are personal and rooted in identity; (b) Instructors—their role and impact; (c) Classmates—a mixed bag; (d) Perceptions of MC and SJ courses; (e) Perceptions of clinical supervision; (f) Systemic engagement with MC and SJ principles; and (g) The emotional and psychological burden of MC and SJ training. Findings are discussed considering sociocultural practices in North America, and MC and SJ training implications are explored.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.018 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".