Evidence‐based solution to information sharing between law enforcement agencies
Bibliographic record
Abstract
Purpose The aim of this study is to test a technological solution to two traditional limitations of information sharing between law enforcement agencies: data quality and privacy concerns. Design/methodology/approach Entity Analytics Software (EAS) was tested in two studies with North American law enforcement agencies. In the first test, duplicated cases held in a police record system were successfully identified (4.0 percent) to a greater extent than the traditionally used software program (1.5 percent). This resulted in a difference of 11,954 cases that otherwise would not have been identified as duplications. In the second test, entity information held separately by police and border officials was shared anonymously between these two organizations. This resulted in 1,827 alerts regarding entities that appeared in both systems; traditionally, this information could not have been shared, given privacy concerns, and neither agency would be aware of the relevant information held by the other. Data duplication resulted in an additional 1,041 alerts, which highlights the need to use technological solutions to improve data quality prior to and during information sharing. Findings The current study demonstrated that EAS has the potential to merge data from different technologically based systems, while identifying errors and reducing privacy concerns through anonymization of identifiers. Originality/value While only one potential technological solution (EAS) was tested and organizations must consider the potential expense associated with implementing such technology, the implications resulting from both studies for improved awareness and greater efficiency support and facilitate information sharing between law enforcement organizations.
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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 | 0.132 | 0.450 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".