Community perceptions of causes of violence against young women in Botswana: fuzzy cognitive mapping
Bibliographic record
Abstract
Violence against young women is a problem worldwide. Understanding its causes in a particular setting can inform context-specific interventions. We used Fuzzy cognitive mapping (FCM), a visual method for collating local knowledge about causes of health outcomes, to explore community views of factors that cause or prevent violence against young women in rural communities in southeast Botswana. In three communities, groups of young men, young women, older men, and older women built maps (68 participants and 12 maps in total) of factors they believed increased or decreased the risk of violence against young women. Trained local facilitators guided group sessions, drawing the reported factors as nodes linked by weighted arrows indicating the direction and strength of causal relationships among factors. Fuzzy transitive closure calculated the influence of each factor on others, considering direct and indirect connections. We combined maps by groups of stakeholders and condensed individual factors into categories which emerged from an inductive thematic analysis. The categories labelled conflict in relationships and parenting and family issues had the strongest influences on increasing violence across all maps. These categories were also common intermediaries between other causal categories and violence. The categories labelled women being disrespectful or uncooperative and transactional and intergenerational sex were the third and fourth strongest risk categories overall. Prominent protective concepts included a stronger legal framework and strengthening the role of local traditional leadership, with greater prominence on the maps of older participants. The most influential risk and protective categories were consistent across young men, young women, older men, and older women. FCM was feasible and acceptable with different stakeholders in Botswana. Fuzzy cognitive maps can inform community discussions, for example, of conflictive gender norms, family dynamics or healthier relationships, and are useful to build theories on how to act on the causes of violence against young women.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".