Is online fraud just fraud? Examining the efficacy of the digital divide
Bibliographic record
Abstract
Purpose Fraud is not a new offence. However, the recent evolution and proliferation of technologies (predominantly the internet) has seen offenders increasingly use virtual environments to target and defraud victims worldwide. Several studies have examined the ways that fraud is perpetrated with a clear demarcation between terrestrial and cyber offences. However, with moves towards the notion of a “digital society” and recognition that technology is increasingly embedded across all aspects of our lives, it is important to consider if there is any advantage in categorising fraud against the type of environment it is perpetrated in. This paper aims to discuss these issues. Design/methodology/approach This paper examines the perceived utility of differentiating online and offline fraud offences. It is based upon the insights of thirty-one professionals who work within the “fraud justice network” across London, UK and Toronto, Canada. Findings It highlights both the realities faced by professionals in seeking to ether maintain or collapse such a differentiation in their everyday jobs and the potential benefits and challenges that result. Practical implications Overall, the paper argues that the majority of professionals did not feel a distinction was necessary and instead felt that an arbitrary divide was instead a hindrance to their activities. However, while not useful on a practical front, there was perceived benefit regarding government, funding and the media. The implications of this moving forward are considered. Originality/value This paper provides new insights into how fraud justice network professionals understand the distinction between fraud offences perpetrated across both online and offline environments.
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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 | 0.037 | 0.175 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".