COVID-19 Lockdown: A Fertile Ground for Gender-Based Violence in South Africa
Bibliographic record
Abstract
Gender-based violence (GBV) has always been an issue of concern in South Africa and Globally. This problem of gender-based violence is currently exacerbated due to the lockdown restrictions. Women and children are targets for Covid-19 related frustrations as gender-based violence reports increase during the lockdown period. Across the country, civil society groups, gender-based violence advocacy organisations, religious groups, and other social justice groups have reported an increase in incidences related to violence against women and children and heightened demand for emergency shelters. Moreover, at the beginning of June 2020, Nehawu mentioned that reports suggested that the number of gender-based violence cases had risen by 500 per cent since the start of the Covid-19 lockdown. Employing the literature review, the paper elaborates on the nature of gender-based violence reported in South Africa and further highlights how gender-based violence has increased during the lockdown period. In this paper, I use the social dominance theory to understand the escalation of gender-based violence when people ought to be focusing on curbing the spread of the COVID-19 pandemic. Observing COVID-19 protocols and encouraging one another to adhere to protocols in the endeavour to reduce the spread of the pandemic ought to be the focus of everyone irrespective of their gender. The suggestions on how to eliminate the scourge of gender-based violence with the advent of the COVID-19 are herein presented.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".