Bibliographic record
Abstract
A growing body of scientific evidence is uncovering how toxic stress and early traumatic experiences have profound long lasting effects on our children’s developing brains and neuro-immune-endocrine systems and are linked to nine out of ten of the most common causes of death in Canada. Domestic violence is linked to many of these effects and although widespread throughout Canada, it receives little attention. In fact, the legal system, the family court system in particular, ignores this medical evidence thereby contributing to the trauma of children. In this thesis I identify and confront eight prevailing myths and biases that create an unfair playing field for women in family court and society and the crisis of justice in Canada. Domestic violence is about power and control over another and I use the lens of the power and control wheel which recognizes eight ways that men use to dominate over women, only one of which involves physical violence. As statistics, reports and medical evidence haven’t been enough to advance actions to address domestic violence on a meaningful level, I use my own story to highlight how this plays out in real life in the hopes of illustrating the urgency of addressing domestic violence in our neighbourhoods. Violence against women requires challenging some deeply held biases and I suggest a more Indigenous perspective on child rearing to help address and mitigate the concerns raised by the Adverse Childhood Experiences 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.047 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".