Using a Vignette in Qualitative Research to Explore Police Perspectives of a Sensitive Topic: “Honor”-Based Crimes and Forced Marriages
Bibliographic record
Abstract
This article examines how a vignette presented to participants during qualitative research interviews was successful in gathering information on the perceptions of 32 police officers and 14 civilians regarding “honor”-based crimes and forced marriages within the context of domestic violence. To my knowledge, this is one of the first methodological papers that presents the process of using a vignette with police on such a sensitive topic. This article offers a reflexive account of some of the methodological considerations I made when constructing the vignette that likely impacted its success. I describe the vignette, discuss how participants reacted to it, and present the themes that emerged to show how it was understood. I then emphasize how first responders engaged in the interview process with the vignette material and how this allowed for a rich, in-depth discussion on an understudied topic. Finally, I discuss the strengths and limitations of this method and make recommendations for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".