What experts say about domestic violence: constructing Thailand’s domestic violence severity index through an expert judgement approach
Bibliographic record
Abstract
Purpose While many countries have adopted traditional approaches to identify patterns and trends in domestic violence at a national level, a strategy that provides more insightful information is still lacking. In response to this need, the purpose of this study is to propose the construction of a domestic violence severity index (DVSI) as an alternative. This index serves as a strategic instrument for policymakers and law enforcement agencies, enabling them to monitor changes in the overall severity of domestic violence incidents over time, beyond relying solely on the volume of reported incidents. Design/methodology/approach Reported domestic violence incidents are collected over the past five years (2019–2023) from the entire country. Unlike sentence-based approaches such as the Cambridge Crime Harm Index and the Canadian Crime Severity Index, the DVSI applies a crime severity index based on expert judgment to assess the seriousness of domestic violence categories. Twenty-three experts with extensive experience in domestic violence issues across various governmental and nongovernmental organizations participated in providing assessments. To ensure consistency in assigning weight values to the domestic violence categories, the average scores provided by experts were calculated using arithmetic mean, median, mode and geometric mean. Findings Domestic violence maps reflecting trends between 2019 and 2023 across 77 provinces in Thailand have been generated based on the index data. The maps depict significant serial and spatial correlation levels from 2019 to 2023. Practical implications These maps carry significant implications for the country’s domestic violence prevention strategy by offering detailed insights into the geographical locations and periods requiring focused attention and resource allocation from the government. This tool can also aid the public in gaining a better understanding of the prevalence of domestic violence in society, facilitating increased coordination and collaboration among stakeholders. Originality/value Many countries quantify domestic violence using simple methods, such as calculating percentages or measuring incidents per 100,000 population. However, a specific DVSI has not yet been developed to analyze and understand domestic violence trends geographically, which could serve as an additional measure to protect victims. In addition, the study uses an expert judgment approach, a rare method in constructing a crime severity index, especially in the context of domestic violence.
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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 | 0.039 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".