Structural Context and the Role of Positive Deviance From Community Norms in Shaping Risk for Sexual Intimate Partner Violence in 32 Low- and Middle-Income Countries
Bibliographic record
Abstract
Community norms provide social scripts and pathways to accruing social capital that can alter a woman's risk of experiencing sexual intimate partner violence (IPV). These norms are in turn influenced by the structural environment in which they exist. Missing from the literature is an understanding of how an individual's departure from community norms-positive deviance-influences the risk of sexual IPV and how this effect may vary across structural environments. Demographic and Health Survey data from 32 low- and middle-income countries (LMIC) were stratified into six structural environments by two fundamental structural factors: level of gender inequality and prevalence of sexual IPV. To examine how transcending community norms shape the odds of reporting sexual IPV across environments, six identical multilevel models were fit including statistical deviation from 13 community norms as key covariates. Positive deviance from community norms is associated with both increased and decreased odds of reporting sexual IPV and the nature of these relationships vary by structural environment. Positive deviance had a greater effect on reporting sexual IPV in highly unequal societies. Positive deviance from fertility preferences and controlling behavior was associated with increased odds of sexual IPV across contexts. The accrual of social capital and differences in female autonomy across environments may be two ways positive deviance alters sexual IPV risk. A better understanding of how the salience of community norms varies by structural environment and how transcending these norms shapes the risk for sexual violence may help highlight pathways for interventions to change restrictive social norms and increase female empowerment without increasing the risk of sexual IPV.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".