Crime Script Sequencing: An optimal forensic combination for cold case analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Criminal cases go cold when investigative leads or forensic testing does not lead to a successful arrest. In these cases, investigators are often keen to use novel methods to derive fresh ideas or insights. Recently, academics from a range of fields, including Psychology, Criminology, and Forensic Sciences have developed a range of new methods and tests to assist with police investigations. The current paper outlines a novel approach to assisting with police cold case investigations: Crime Script Sequencing. The new method combines two leading temporal methods, Crime Script Analysis and Behaviour Sequence Analysis. A real-world cold case, the bombing of Canadian Pacific Airlines Fight 21, is presented and analysed using Crime Script Sequencing to offer readers a guide of how to use the method for other investigations. Impacts, insights, and potential future developments of the method are outlined.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it