ScriptNet: An integrated criminological-network analysis tool
Bibliographic record
Abstract
Abstract This brief article illustrates the features of ScriptNet, a software package that facilitates a visual analysis of the organisational aspects of criminal enterprise, together with a visual analysis of the network of people, organisations, places and resources that are in some way involved in the commissioning of these goal-oriented crimes. ScriptNet is an amalgamation of the terms ‘script’ and ‘network’ that in turn represent two analytical approaches to understanding criminal and social behaviours. Script refers to crime script analysis, an analytical technique that organises knowledge about the procedural aspects and procedural requirements of the crime commission process. Network derives from social network analysis, and specifically from the framework of multi-mode and multi-link networks, which maps individual and collective actors, together with resources they can access and places where they are located, and the various types of relationships that may link them. In this article we illustrate the functions and features of ScriptNet using data provided by the Food Safety Authority of Ireland (FSAI). We discuss the innovative aspects of ScriptNet and we identify its limits. In its current format, ScriptNet has been developed as proof of concept. The code is open source, and we welcome people to collaborate and implement new and improved functions.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.007 |
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".