An intergenerational decolonizing path to healing: envisioning change with Indigenous mothers and girls.
Bibliographic record
Abstract
Programs and interventions designed to reduce or remedy social, political, economic, and health disparities have varying levels of both short-term and long-term success. Within First Nations and Metis communities in Manitoba, health status and wellbeing, especially of women and children, is an ongoing concern, influenced largely by history, culture, and systemic experiences. Women and girls were recruited to participate in an activity based research project to answer the question: “what do you need to be happy, healthy and safe and how do you try to make sure that girls grow up to be happy, healthy and safe?”. Recruitment criteria included self-identification as First Nations or Metis, a mother (or other female family member providing care) (n=24) of girls ages 8-12 (n=36), lived within Winnipeg, and the ability to commit, with her daughter, to an evening a week for seven weeks. Three workshops were conducted between September 2015 and March 2016. This study employed two key methodological study design components. A participatory workshop provided space for shared learning and intergenerational engagement while a community based research approach utilizing process and evaluation design components allowed for the collection and initial analysis of data within the workshops. Through activities, such as crafts, games, and discussions, participants shaped this study. The predominant theme that emerged was that in order to achieve health, safety and wellbeing, historical trauma needs to be addressed. This is a grouped manuscript style thesis. Chapter one provides an introduction to the study. Chapter two explores how the methodological approach provided a space for reconciliation, self-determination and healing. Chapter three explores the concept of harm reduction within family contexts as demonstrated through three arts-based activities. Chapter four provides a theoretical discussion about historical trauma, cultural memory and the workshop environment as a space for testimony and witnessing. The final chapter depicts a series of dissemination products and the incorporation of ethical research practices used to ensure that products were useful, and relevant for participants (women and girls), as well as their families. The thesis concludes with possible future directions for research, policy implications, and final thoughts about the study.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".