Bibliographic record
Abstract
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.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".