Cartage killers: considering highway serial homicide as a novel offender typology
Bibliographic record
Abstract
With vocation-specific serial offenders being a relatively understudied phenomenon, the increased (and overdue) scrutiny of the intersectionality between the US long-haul trucking industry through initiatives such as the FBI's now well-publicised Highway Serial Killer Initiative (HSKI), along with associated mainstream media reportage regarding this same phenomenon in the context of specific cases, raises questions about the motivational models of such offenders, their common psychological characteristics, and how to consistently define them. By way of comparison, healthcare serial killers have long been recognised in the literature and among both law enforcement and regulatory bodies as a distinct subtype of serial offender and there are correspondingly an array of specific investigative methodologies and failsafes now used to investigate and ameliorate their associated crimes. In this article, we seek to designate 'cartage killers' (serial offenders variously employed in logistics and Interstate long-haul tractor-trailer trucking) as a similarly specific phylum of perpetrator and to also establish corresponding nomenclature through a synthesis of the existing multidisciplinary literature.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".