Seeing crime, feeling crime: Visual evidence, emotions, and the prosecution of domestic violence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Changes in prosecutorial strategies vis-a-vis domestic violence introduced new models of investigation that privilege images of victims. Drawing on case law, we argue these visual artefacts of victims’ injuries as well as their videotaped sworn statements describing their assaults constitute what Haggerty and Ericson call a ‘data double’, a virtual doppleganger who is meant to stand, often antagonistically in the stead of the flesh and blood victim. We further suggest, following theorizing on the emotional impact of images, that these pictures and videos, presented in court, have an emotional stickiness that differently affects both judges and juries as compared to the testimony of the flesh and blood victim. Thinking through temporality and notions of femininity we conclude that the truth effect of these images is that the victim’s data double becomes more human than human, forcing us to rethink the relationships between victims, images, and the machinations of justice.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it