Understanding Forced Marriage in the United States: Developing Measures, Examining its Nature, and Assessing Gender Disparities
Bibliographic record
Abstract
In Western European countries and Canada, an emerging scholarly literature has helped inform awareness and prevention efforts and justice system responses to address forced marriages. But, little is known about this issue in the United States. The present study, the first large scale examination of forced marriage prevalence in the United States, surveys a national sample of Internet users over 18 years of age. Using a Google Consumer Survey platform, this exploratory study seeks to estimate prevalence of and gender disparities in forced marriages for this sample; examine conditions and circumstances associated with forced marriage; develop adequate measurement tools to identify individuals who have faced forced marriage; and better understand how and to what extent those experiencing forced marriage face psychological abuse from intimate partners and access victim services. Contrary to the experiences of legal and social service providers, findings indicate that more men than women reported forced marriage experiences. The discrepancy between this finding and prior research underscores the need to further develop survey measures and reconsider closed-ended, web-based survey screener questions such as the one used to identify respondents experiencing forced marriage in this study. These considerations may help identify victims with better precision and locate the underlying causes of gender disparities in forced marriage. In addition, many respondents reported entering forced marriages while facing concerns over their own or their family's reputation and threats of harm (to themselves or self-harm of a family member).A majority of respondents also reported experiencing psychological intimate partner violence and seeking help, particularly from law enforcement and for medical assistance. This large overlap between forced marriage and psychological intimate partner violence, as well as between forced marriage and seeking help from law enforcement and medical assistance providers, indicates that service provision needs are notably acute for this population..
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".