Exploring the use of geographic methods to understand sexual- and gender-based violence: a scoping review
Bibliographic record
Abstract
Geographic methods of inquiry are being increasingly employed to discern and visualize geographical patterns associated with increased risk of sexual- and gender-based violence (SGBV). As such, this scoping review systematically collated academic literature and subsequently synthesised (1) the time trend of studies employing geographic methods to understand SGBV, (2) the context in which geographic methods are being used to understand SGBV (i.e. objectives), (3) study characteristics, and (4) the methods and data sources used. A total of six databases were searched: Gender Studies, PsychINFO, Scopus, PudMed, Cochrane, and Campbell. Following title-abstract (n = 3354) and full-text screening (n = 159), 42 studies met data extraction criteria. From our review, there is clear momentum in the use of geographic methods to understand trends in SGBV. The majority of studies stated objectives aligning with assessing risk of SGBV (n = 35, 83.3%), while the remaining aimed to assess SGBV service availability. As research expands, there is notable focus on urban and suburban areas and a dominant dependence on institutional data sources (e.g. hospitals and police data), which hold certain caveats when it comes to structural barriers to SGBV data collection, such as fear of reporting and historic distrust in institutional services. Dovetailing the employment of big data sources with community-facilitated SGBV data collection methods may be a promising avenue for neighbourhood-specific efforts to inform policy and practice.
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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 | 0.055 | 0.217 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.053 | 0.047 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".