Digital Footprints of the Policing Web: A Study of Police Economic Crime Investigation using Natural Language Processing and Machine Learning
Bibliographic record
Abstract
Over the past two decades we have witnessed an increasing allocation of criminal justice resources to the control of economic crime around the world. Despite the criminalization of various harmful economic activities in Canada, such as money laundering, illegal insider trading, and corporate bribery, there is a persistent gap between legislative intent and actual enforcement. I argue we have been stuck in a recurring cycle of reform since the 1960s. There is a general sense that more ought to be done to tackle problems of economic crime in Canadian society. Yet when the justice system’s inability to handle complex cases of economic crime rises to the level of political controversy, the lack of careful, empirical research hampers efforts to implement durable and effective reforms. To address this challenge, I adopt a novel methodological approach that blends conventional qualitative and quantitative techniques with methods of natural language processing and machine learning. This dissertation contributes to the fields of criminology and public policy by conducting a comprehensive examination of the state of police economic crime investigations in Canada. I focus on three interrelated dimensions of economic crime policing, which I conceptualize as investigative challenges, public images, and state resolutions. My analysis of investigative challenges explores the difficulties faced by police detectives when conducting investigations into complex forms of money laundering, capital markets crime, and state-corporate corruption. My examination of public images and state resolutions delves into how police economic crime investigations are portrayed in news media and official discourse, uncovering a tendency to oversimplify narratives in ways that hinder meaningful reforms. Central to each chapter is the assertion that employing innovative computational methods for social scientific research provides a productive approach to diagnosing challenges in the criminal justice system, especially in those components that are harder to research using traditional methods, including detective units. By providing new avenues for understanding and reforming issues in Canadian policing, this project contributes to the ongoing discourse on the future of criminal justice in the 21st century.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".