Raising Expectations: Scrutinising Violence Against Women Policies using the WHO Respect Women Framework as a Measurement Tool.
Bibliographic record
Abstract
Violence Against Women is a human rights issue that impacts approximately 30% of women and girls worldwide (WHO, 2021). The increase of Violence Against Women cases and a disregard for relevant data have become a cause for concern for women, minority groups and the criminal justice community. This research examines the response to VAW and use of VAW policy through the questions “What measures have Ireland, Canada, and Australia taken to address and reduce Violence Against Women?” and “To what extent do Violence Against Women policies and measures in Ireland, Canada, and Australia meet the WHO Respect Women Framework’s criteria for success?” Using documentary analysis, a comparative approach and secondary data, the study examines the measures Ireland, Canada, and Australia have taken to address and reduce Violence Against Women under their respective Violence Against Women policies. This research applies the WHO (2019) Respect Women Framework to Violence Against Women policies in Ireland, Canada, and Australia to understand the extent frameworks addressing Violence Against Women are adopted to policies. The findings suggest that Ireland, Canada, and Australia have taken several measures to address and reduce Violence Against Women under their current Violence Against Women policies. However, due to Canada’s policy being implemented five years before Ireland and Australia, more measures were evident in Canada. The research found that according to the twenty-two recommendations to address and reduce Violence Against Women under the Respect Women Framework, Ireland met sixteen requirements, Australia met fifteen and Canada met ten. The study suggests that there are several discrepancies between measures taken to address Violence Against Women, Violence Against Women policies and the risk factors associated with Violence Against Women and makes several recommendations to bridge this gap.
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 | 0.069 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".