Technology as a source of complexity and challenge for special victims unit (SVU) investigators
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.
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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 | 0.024 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 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".