Bibliographic record
Abstract
<p>Traditional viewpoints held by academic and non-academic professional groups of the white-collar crime offender profile(s) are that they are non-violent. Yet research has begun to unveil a sub-group of white-collar offenders who are violent, referred to as red-collar criminals, in that their motive is to prevent the detection and or disclosure of their fraud schemes through violence. This article is the first to discuss the origin of the red-collar crime concept developed by this author coupled with debunking white-collar offender profile misperceptions that have persisted for decades by offering current research on the anti-social qualities displayed by this offender group that predates their violence. Secondly, the article applies behavioral risk factors, such as narcissism and psychopathy, which contributes to our understanding of why some white-collar offenders may resort to violence while other white-collar offenders do not. Case analysis also draws upon gender distinctions, workplace violence and homicide methods used to illustrate that red-collar criminals are not an anomaly to ignore simply because they may not reflect the street-level homicides typically observed by society, investigated by law enforcement and studied by academia.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".