The Inherent Limitations of Gender-Based Violence to the Exclusion of LGBTIQ: A Guide for Social Workers
Bibliographic record
Abstract
Drawing from the queer theory, this article strives to understand the scourge of gender-based violence against members of the homosexual community through a literature review. There is a gap in understanding this scourge against LGBTIQ. Additionally, there is a dearth of research on GBV within the social work fraternity despite the profession’s mandate to protect vulnerable groups. A comprehensive understanding of this hate crime is critically important in the current times where incidents of violence based on one’s gender are on the rise in South Africa. Social workers are in a privileged position to educate communities about these appalling crimes and to inform inclusive policies to curb this pandemic against members of the homosexual community.
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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 | 0.024 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".