The Criminal Ecology of Payment Systems: How ‘Identity Theft’ Evolved from Plastic Counterfeiting to ‘Crime of the Century’
Bibliographic record
Abstract
In one of his classical articles, Edwin Sutherland highlighted as early as 1940 the mutual benefits that economists and criminologists could derive from a better integration of their knowledge. Although his interdisciplinary invitation was aimed at white collar crime, other forms of delinquency are particularly well suited to a similar approach, “identity theft” being among them. After having examined in the first section of this article the ambiguities and paradoxes that apply to contemporary analyses of “identity theft”, I offer an alternative analytical framework that relies on the concept of coevolution between “identity theft” as we know it and the development of a new payment system from the late 1950s. By using historical and economics data, I show how this fundamental transformation in consumer habits contributed to the emergence of new criminal opportunities, the development of new security measures and private conflict resolution procedures, as well as the decline of other types of crimes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.052 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".