ScriptNet: An integrated criminological-network analysis tool
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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