Eradicating the Pandemic of Violence against Women (VaW) during COVID-19: the critical imperative for health: Violence against women during COVID-19
Bibliographic record
Abstract
Prior to COVID-19, the #The MeToo movement took the world by storm, exposing the extreme suffering of women at the hands of abusers. The United Nations (UN) has described Violence Against Women (VaW) as “perhaps the most shameful human rights violation” VaW is a longstanding global public health problem which has been ignored despite the efforts of many. Survivors of VaW are facing disproportionate consequences due to COVID-19 and resulting lockdowns and economic hardship worldwide. As a result of COVID-19, the reality of significant morbidity and mortality is gaining more attention, particularly as VaW is increasing. In this policy brief, we address issues related to VaW and COVID-19 through a social justice lens that applies a feminist anti-racism analytical framework. We argue that this critical time period can be used to catalyze long lasting changes to prevent and mitigate VaW through comprehensive short and long term policy measures related to education, research, media coverage, legislation, policing, social work and so forth. It is urgent that governments everywhere make women and children safety an immediate priority through the provision of safe housing, food security, healthcare and retraining for livelihoods. The pandemic of VaW must never be silenced again and movements such as #MeToo ought to be supported to promulgate effective human right changes that lead to systemic and institutional justice. Because of the intensification of VaW during this time, Covid-19 offers the world the opportunity to eradicate VaW once and for all. Eradicating VaW is a complex endeavor which requires buy-in from all sectors. Here, we consider the complex intersection of issues creating the current climate of heightened violence against women during Covid-19. Global leaders in government, business, and other sectors, in addition to local community members, ought to make efforts to protect women’s lives and shift the public narrative related to VaW. Empowering boys and men to prevent and combat VaW is a critical part of this work. Toxic masculinity, which is defined as widely accepted gender norms about men’s authority and men’s use of violence to exert control over women, is one of the deadly roots of VaW. Everyone on the gender spectrum has a role to play in ending the deadly pandemic of VaW.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".