Gender-Based Violence in Kosovo during the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract COVID-19 has exacerbated health inequalities around the world. Kosovo has so far experienced four waves of the pandemic with a fatality rate of 2.6 registered deaths per 100 cases which is higher than some comparable countries in the region. Women have been disproportionally affected in many spheres of life including their safety and security at home. While Gender-Based Violence (GBV) has been one of the major concerns for women’s safety over the years, the COVID-19 pandemic has further exacerbated the situation. Drawing on the theory of GBV and intersectionality and using a mixed-method approach, this study examines whether GBV cases have increased during the COVID-19 pandemic and whether government policies and responses during the COVID-19 pandemic have considered GBV implications. This study yields three main findings: First, the institutional data on reported cases show that GBV has increased significantly between 2010 to 2021. Similar trends of increase were observed during the COVID-19 pandemic. Second, the COVID-19 institutional actions towards the pandemic disproportionally considered the specific needs of the most vulnerable groups of the population including women. Third, violence against women is treated within the domestic violence domain which does not address entirely the nature of the gender-based violence in the country.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".