Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the discourses surrounding accountability as it relates to fraud victimisation. Design/methodology/approach This paper is based upon interviews with 31 professionals across the fraud justice network (FJN) in the UK and Canada. Findings The paper highlights the complexities that surround participant’s perspectives of liability when it comes to fraud. Professionals articulated responsibility falling across the spectrum of victims, offenders and third parties. Further, it is evident that perspectives of accountability are largely influenced by the various types of frauds that exist and the ways in which victims incur losses. Research limitations/implications Interviews with selected FJN professionals may not be representative of those across the broader sector in each country. Despite this, there was still a diversity in views which highlights the tensions that currently exist as to where accountability is positioned. Practical implications The findings clearly indicate that accountability is not directed at any one party, rather there appears to be an interplay across offenders, victims and third parties. While the offender is arguably the one who should be held most accountable for their actions, a lack of official responses to fraud offending means that the offender is largely invisible. For those who place accountability on the victim, there is evidence of neoliberal discourses that argue for prudential citizens, or those who take responsibility for their own actions. This is in contrast to those who believed that victims could not be held accountable for actions beyond their control, and instead third parties were accountable, and should increase their role in education and awareness. Originality/value This paper articulates the discourses of accountability that exist for fraud, and how the current thinking can contribute to interactions with victims, as well as current responses to victimisation. Further work is required to better identify the criteria against which victims are being held accountable, as well as better understand who bears responsibility with responses to fraud victimisation.
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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 | 0.044 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.028 | 0.067 |
| Scholarly communication | 0.022 | 0.023 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".