Technology as a source of complexity and challenge for special victims unit (SVU) investigators
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
Although there has been significant public and academic interest in the ability of police to harness new technologies in order to solve crimes, there has been significantly less focus on how the proliferation of new technologies has impacted police workloads. In this exploratory study, we begin the process of rectifying this oversight by exploring some of the challenges mobile technologies pose to investigators working in a special investigations unit. Our work is informed by an analysis of data collected through in-depth interviews with police investigators to address the following research question: “To what extent has the complexity of special victims (sex crimes) investigations changed over time?”. Our findings indicate that technology is the most prominent factor leading to increased complexity of investigations. Specifically, technology adds to the volume of evidence that must be examined and managed, rapid advances in technology require additional training and expertise, and despite technological advances to assist in investigations, the process remains largely manual.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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