“You Can’t Get There from Here”: Use of Crime Scripts in Validity Testing
Bibliographic record
Abstract
Abstract Purpose Detectives require analytic tools for the evaluation of deception, truth, and probability as police investigations need to assess the validity of suspect alibis, witness claims, victim allegations, and crime theory feasibility. Design For a crime to happen, a number of preliminary, intervening, and follow-up steps have to occur (e.g., finding a target, casing the bank, disposing of the body) along the dimensions of time, action, and geography (TAG). A crime script is a framework for dissecting this sequence. We propose their use for assessing the feasibility of a crime narrative. If the required phase shifts are improbable, or the order of actions illogical, then such an analysis warns investigators the TAG line is problematic. Findings Different case studies – a wrongful conviction, a murder trial, and a social media crime frenzy – are dissected and evaluated using crime scripts. The analyses reveal the improbability of all three crime narratives. Practical Implications Crime scripts are a useful thinking tool in criminal investigations. By deconstructing a crime into discrete temporal, geographic, and action phases, the viability of the overall narrative can be assessed. If a reasonable story cannot be constructed from the linked stages, there is a problem and further inquiries are required. Originality We propose a novel application of crime scripts to assist in police investigations. Despite the importance of validity and veracity assessment in this task, the area remains an understudied part of the criminal investigation process.
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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 | 0.134 | 0.517 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".