Pathway 7 Co-Creating Macro–Mesolevel Change
Bibliographic record
Abstract
Pathway 7 focuses on systems-level change processes. Melissa, Sandra, and Jane reiterate the importance of assuming a contextualized lens in building shared understanding of client lived experiences by considering carefully social determinants of health and opening possibilities for micro-, meso-, and macrolevel change. They reiterate the approach to social justice introduced earlier in the book that centres justice-being, encouraging therapists to lean into decolonizing themselves and their practices. Then they introduce a framework for (a) justice-seeking, which involves advocating for structural–societal change and (b) justice-doing, which focuses on institutional–organizational change. Jane draws readers into consideration of macro–mesolevel change in Pathway 7 through thoughtful and honest reflections on their own lived experiences at the intersections of systemic power, institutional policies, relational accountability, and personal cultural identities. Jane begins by reflecting on their white, settler lineage and the ways in which they have benefitted from the economic, political, social, and psychological violence of colonialism. They extend their reflections on their positionality to critically examine constructs of genders, gender identities, sexes, sexualities, and pronouns in the context of the current oppressive and exclusionary discourses that misconstrue gender as only two sexes. Jane’s reflections lay a foundation for the intent of this pathway to advance a more just, accessible, inclusive, diverse, and equitable society for all people and peoples.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".