Effects of Gender-Based Violence Towards Young Females: The Case of Vhufuli Village in Thohoyandou, Limpopo Province-South Africa
Bibliographic record
Abstract
Gender based violence towards young women is a pandemic experienced mostly by women of all classes and age in different settings, spheres of life and environment.It affects the victim both socially, emotionally, psychologically and physically.Gender based violence is caused by various factors such as substance abuse, lack of education as well as gender norms, socialization and aggressive behavior of men.This study explored the experiences of young women regarding gender-based violence and factors that contribute to this scourge.The research was qualitative in nature and used non-probability sampling as well as snowball and purposive sampling to gather the data.The population was young women between the ages of 12-25 from Vhufuli village.Data was collected using semi-structured interviews.The key finding of the study is that young women are experiencing sexual abuse, physical abuse, emotional abuse, economic and psychological abuse at the hands of their partners and parents.The recommendations are that policy makers need to regulate laws that will also be enforced, as a way of fighting against gender-based violence.There is a need to educate our communities about the dangers posed by patriarchy, and to also understand how hegemonic masculinities can be toxic in our communities.Social workers could also help by coming up with early intervention strategies that may assist in curbing the pandemic of domestic violence.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".