Intersecting contexts of oppression within complex public systems
Bibliographic record
Abstract
This chapter is a theoretical discussion developed by two PhD-prepared nursing professors. Our thinking is embedded in more than 55 years of mental health nursing practice and academic experience. Many of these years were spent working with children, youth, adults, families and communities in crisis – those who bump into the most pointy edges of life and society. We have walked along with street youth, domestic violence survivors, refugees, young offenders, women preparing for criminal proceedings and people who misuse substances. We have worked and volunteered in community-based non-profit organisations, hospitals, emergency services, community mental health clinics, provincial and federal governments, and the World Health Organization. We have worked primarily in Canada, but have also been touched by the most vulnerable in communities in Mexico, Cuba and India. We have extensive backgrounds in the application of social sciences to health issues. Our theoretical standpoint is that of critical feminist theory based in realist ontology/epistemology and complexity science, and we use an intersectionality lens to draw this thread through our discussion. Our strong practice and academic backgrounds ground our thinking in interrogating oppressions and their intersections and public system complexity as they relate to criminal justice. In our academic and practice work, we focus on the social determinants of health (SDH). The primary factors that shape the well-being of individuals, families, communities and nations are not medical treatments or lifestyle choices, but rather the living conditions they experience (Mikkonen and Raphael, 2010). These factors are known as the SDH: employment and working conditions; income and its equitable distribution; education and early childhood development; housing and food security; age; gender; and race. The SDH are also related to the extent to which citizens are ‘provided with the physical, social, and personal resources to identify and achieve personal aspirations, satisfy needs, and cope with the environment’ (Raphael, 2009, p 56). According to the World Health Organization (2008, p 1), the SDH are important markers of inequalities in health and well-being: The poor health of the poor … is caused by the unequal distribution of power, income, goods, and services, globally and nationally, the consequent unfairness in the immediate, visible circumstances of peoples’ lives – their access to health care, schools, and education, their conditions of work and leisure, their homes, communities, towns, or cities – and their chances of leading a flourishing life.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.021 | 0.111 |
| Scholarly communication | 0.027 | 0.023 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".