“If it is a tear let it be a tear, not a laceration” : form J88 as evidence in prosecution of violence against women in South Africa
Bibliographic record
Abstract
The availability of the J88 form in court is believed to convey the precise clinical description of \nthe woman’s injuries as it is seen as prima facie evidence. This article reports how the J88 form \nis used in prosecution of violence against women (VAW). A four-phased sensory ethnographic \ndesign that used courtscapes, participants’ observation, document analysis and conversations \nwith prosecutors and court personnel to generate data was employed. In this paper the focus will \nbe on findings from conversations and reviews of relevant documents. The findings indicate that, \nregardless of J88 being legally endorsed as prima facie and standalone evidence, some trials of \nVAW cases continue without it. Most importantly, J88 forms presented for evidence are usually \n‘silent’ as they don’t have any impact on prosecution of VAW. In some VAW cases, the J88 forms \nare viewed as recall for a victim’s condition. We recommend a synergistic approach that is transdisciplinary \nin nature in documentation of J88 forms. Such documentation will advance the legal \nand health practices.
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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".