The Changing Role of the Law Enforcement Analyst: Clarifying Core Competencies for Analysts and Supervisors Through Empirical Research
Bibliographic record
Abstract
Enhanced opportunities to use new techniques to derive insights from large volumes of data have potential to change the role of the law enforcement analyst. Alongside this growing potential is a push toward professionalization for crime and intelligence analysis as an occupation. In this paper, we explore the extent to which existing descriptions of core competencies reflect skills identified as important by analysts and their managers. We draw on interviews with sixty-one analysts and supervisors from law enforcement agencies in Canada, the United States, and Australia. We confirm that existing descriptions of core competencies align with those identified by analysts and managers. We identify three considerations for present-day police organizations: the importance of data literacy and technical skills, the importance of self-motivation as a trait for analysts, and the core competencies of analyst supervisors. We complicate discussions about the importance of technological comprehension in light of software that automates components of analysis, illustrating how limits in data literacy translate to challenges for making effective use of new technologies. Next, we demonstrate how an ability to navigate interpersonal dynamics in police organizations is essential for analysts and analytic managers to be effective in their roles. We illustrate a reliance on individual-level motivation for competency-building over organizationally-driven or standardized professionalization. Finally, we contribute to limited scholarship addressing competencies for analyst supervisors and managers, and discuss the implications of supervision for analyst skill development.
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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 | 0.041 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".