Patriarchy at the helm of gender-based violence during COVID-19
Bibliographic record
Abstract
Gender-based violence (GBV) or violence against women and girls (VAWG), is a global pandemic that affects 1 in 3 women in their lifetime and VAWG is one of the most prevalent human rights violations in the world. The high level of investment going into the COVID-19 recovery plan is a unique opportunity to reshape our patriarchal society, to coordinate across sectors and institutions and to take measures to reduce gender inequalities. Relief efforts to combat the pandemic should take the needs of the vulnerable population, particularly women and girls afflicted by GBV into consideration, as their needs were mostly ignored in the recovery plan of Ebola. GBV is linked to dominance, power and abuse of authority or because any calamity, be it a pandemic, conflict or a disaster. This will further exacerbate pre-existing gendered structural inequalities and power hierarchies as protective mechanisms fail leaves women and girls more vulnerable, fueling impunity for the perpetrators. There is a need for international and domestic violence prevention policies to not only focus on narrowly defined economic or political 'empowerment' because that is insufficient when it comes to challenging existing gender inequalities. Incorporating an individual's religious beliefs and community of faith (mosque, church, temple or synagogue) can offer a support system for an individual and her/his family amid a public health crisis. There is a need to engage men and boys by tailoring messages to challenge gender stereotypes and unequal gender roles to overcome patriarchy.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".