Sifting through the Net: Monitoring of Online Offenders by Researchers
Bibliographic record
Abstract
Criminologists have traditionally used official records, interviews, surveys, and observation to gather data on offenders. Over the past two decades, more and more illegal activities have been conducted on or facilitated by the Internet. This shift towards the virtual is important for criminologists as traces of offenders’ activities can be accessed and monitored, given the right tools and techniques. This paper will discuss three techniques that can be used by criminologists looking to gather data on offenders who operate online: 1) mirroring, which takes a static image of an online resource like websites or forums; 2) monitoring, which involves an on-going observation of static and dynamic resources like websites and forums but also online marketplaces and chat rooms and; 3) leaks, which involve downloading of data placed online by offenders or left by them unwittingly. This paper will focus on how these tools can be developed by social scientists, drawing in part on our experience developing a tool to monitor online drug “cryptomarkets” like Silk Road and its successors. Special attention will be given to the challenges that researchers may face when developing their own custom tool, as well as the ethical considerations that arise from the automatic collection of data online.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".