A Comparative Analysis of Domestic Violence Protection Legislation in Canada
Bibliographic record
Abstract
Domestic violence is a pervasive social problem. It affects all Canadians to some extent, but is especially worrisome for women. "Every hour of every day, a woman in Alberta will undergo some form of interpersonal violence from an ex-partner or ex-spouse" (Wells, Boodt, & Emery, 2012). Although Alberta falls on the higher end of rates of domestic violence compared to other sub-national jurisdictions, it is not uncharacteristic of the rest of Canada (Sinha, 2012). One of the strategies to deal with domestic violence has been the introduction of specialized domestic violence legislation. Nine of the thirteen sub-national jurisdictions in Canada have active specialized domestic violence legislation, and two sub-national jurisdictions had bills for specialized domestic violence legislation that did not get royal assent. The federal government has a bill for specialized domestic violence legislation that was passed through the Senate, and is currently in the House of Commons (Canada P. o., 2012). These statutes all have similarities, but have significant differences in several areas. Specifically they differ in their definitions of violence and what kind of relationship one has to be in, the offence and penalties provision, whether there is an arrest provision, and the duration of orders. This report compares the various legislations, highlights the differences in legislation, and discusses the implications of the diversity of legislation throughout Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.026 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".