Gender-based violence and COVID-19 : how the pandemic has shifted GBV policies, and implications for non-governmental organisations and non-profits
Bibliographic record
Abstract
In this research I ask: How effectively have Canadian federal and provincial policies shifted to meet the needs of Non-Governmental Organisations and non-profits in British Columbia that serve those who have experienced Intimate Partner Violence (IPV), following the COVID-19 pandemic? Secondly, what can be done to improve the situation? Little research currently exists on this subject, as Canada continues to recover from the implications of the pandemic. To best answer these questions, I examined Gender-Based Violence policies over the past several decades created by the federal government and provincial government of British Columbia. In particular, I employed a policy trajectory approach to consider whether policy adaptations were implemented to meet the increased needs of non-profits and NGOs as a result of challenges brought upon by COVID-19. In tandem with policy trajectory analysis, I utilised an intersectional feminist framework rooted in the scholarship of those like Angela Davis, Kimberlé Crenshaw, and Patricia Hill Collins, as well as supporting literature from Indigenous activists who wrote the MMIWG report, work of disability scholars, LGBTQ2S+ researchers, and others. The inclusion of these authors demonstrates the disproportionate experiences of partner violence, as well as the additional barriers members of marginalised communities face when seeking services. Importantly, I drew upon this work to highlight the ways in which government policies have failed to address gaps in support systems for survivors. Ultimately, the findings of my research have underlined the ways in which policies implemented during the pandemic were largely reactive. Instead, recommendations produced here suggest that governments must: (1) proactively engage with non-profits, survivors and other stakeholders to not only stop partner violence, but proactively prevent it; (2) governments at all levels must acknowledge their role within systems of oppression and work collaboratively with impacted communities to address barriers; and (3) government officials find ways to shift leadership to those on the frontlines, as well as survivors, to guide policy creation and consultation moving forward.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".