Practice 12 Anti-Oppressive Care: Celebrating Diversity
Bibliographic record
Abstract
In this chapter Sandra, Kim, and Melissa invite consideration of synchronicities between conventional eurowestern psychological practices and the Seven Sacred Teachings, attending specifically to the Teachings of Truth, Courage or Bravery, and Wisdom. These Teachings centre relationships in health, healing, and care, deepening appreciation of relational reciprocity in the moment-by-moment interactions with clients. The authors invite readers to reflect on the relational values that provide them with an ethical compass to guide anti-oppressive care in counselling and psychotherapy. Building on earlier theoretical foundations (e.g., critical race theory, intersectionality theory, liberation psychology, queer theory, critical feminist theory), anti-oppressive care validates the injustices and inequities embedded within society and invites critical reflection on the subtle yet persistent ways that dominating discourses and oppressive ideologies creep into therapeutic spaces. Kim draws on disability justice theory and practice to centre accessibility in anti-oppressive care throughout the chapter. She focuses, in particular, on digital accessibility, offering tips throughout the chapter to illustrate how small practices can support anti-oppressive service delivery and care. Other themes integrated within the organizational structure of the Seven Sacred Teachings include: safer relationships, microaggressions, internalized ableism, just conversations, identity-affirming language, power analysis, embodied knowing, disablement and harm, Wise Practices, and personal–professional integrity. A many hands perspective on social justice practice opens a range of possibilities for engagement in anti-oppressive care that extends to change at the sociocultural, economic, ecological, and political contexts of clients’ lived experiences. Sandra, Kim, and Melissa appreciate the practice illustrations by the following co-authors: • Judy Chew describes the use of power analysis in her practice example, centring relationship over technique as she follows the client’s lead throughout the conversation. • Allison Reeves provides a practical example of enacting cultural safety, drawing on her extensive experience and mentoring within Indigenous communities.
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 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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.048 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".