Bibliographic record
Abstract
In Pathway 6 Darlene introduces into the book a concept called Wise Practices to share, with gratitude, the understandings already established through an Anishinaabeg way of knowing. Wise Practices offer an ethical and relational framework for culturally responsive and socially just counselling. Drawing on the concept of Wise Practices and the principle of Etuaptmumk, also known as Two-Eyed Seeing, the authors demonstrate how these lenses intentionally contextualize client, family, and community challenges, supporting the co-creation of change processes that are relationally and culturally attuned. This approach disrupts conventional eurowestern paradigms that privilege evidence-based or protocol-driven interventions. Instead a Wise Practices approach equally values practices that honour interconnectedness, reciprocity, and respect for Indigenous and other worldviews. By considering the whole person in context, a Wise Practices stance fosters therapeutic relationships rooted in balance and collective well-being that reflects commitments to relational ethics, Indigenous reconciliation, and the wholistic care of individuals and communities. The chapters in this pathway amplify and illustrate the concept of Wise Practices. This opens the door to embrace other culture-centred views of health, healing, and care, for example, Black Canadian perspectives on an Africentric framework for mental health care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.053 | 0.019 |
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".