How Wearing a Social Justice Lens Can Support You, Your Clients, and the Larger Community: An Intersectionality Workshop With a Twist
Bibliographic record
Abstract
Counsellors may not comprehend fully the impact of their blind spots as a result of unconscious cultural encapsulation. The authors propose a self-reflective method by which counsellors can self-examine their assumptions about diversity and intersectionality. They invite readers to engage with the contents of this article to identify their blind spots, biases, and assumptions through self-reflective exercises. This article summarizes an intersectionality workshop with a twist that was offered by Melissa Jay, Jason Brown, and Rebecca Ward at the 2019 conference of the Canadian Counselling and Psychotherapy Association. The intention of the workshop was (a) to raise consciousness about systemic oppression, (b) to explore Collins’s (2018c) culturally responsive and socially just case conceptualization as the framework for the workshop, (c) to bring client intersectionality to life using four vignettes they created, (d) to reflect on client intersectionality and cultural identity, and (e) to propose a method by which counsellors can self-examine their assumptions about diversity and intersectionality, leading to more culturally competent counselling.
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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 | 0.035 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.048 | 0.051 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.004 | 0.037 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 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".