The problem of “white noise”: examining current prevention approaches to online fraud
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the current prevention messages that exist surrounding the prevention of online fraud. In particular, it focuses on the amount and level of detail that is promoted for each type of potential fraudulent approach. Design/methodology/approach Multiple data sources are used to establish the main premise of this paper. This includes the publication entitled The Little Black Book of Scams , qualitative data from victims who have experienced online fraud, and materials collected through a police investigation into online fraud. Findings Results of this analysis indicate that current prevention messages are characterised by a large degree of detail about the various ways that (online) fraud can be perpetrated. This is argued to be ineffective, based on the experiences of victims who were unable to apply their previous knowledge about fraud to their experiences. Additionally, the categorisation of fraudulent approaches is highlighted as unimportant to offenders, who are focused on obtaining money by whatever means (or approach) possible. Practical implications This paper provides the impetus to evaluate the effectiveness of current prevention messages. It points to a simplification of existing prevention messages to focus more importantly on the transfer of money and the protection of personal information. Originality/value This paper argues that current prevention messages are characterised by too much “white noise”, in that they focus on an overwhelming amount of detail. This is argued to obscure what should be a straightforward message which could have a greater impact than current messages.
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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 | 0.060 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| 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".