Offensive communications: exploring the challenges involved in policing social media
Bibliographic record
Abstract
The digital revolution has transformed the potential reach and impact of criminal behaviour. Not only has it changed how people commit crimes but it has also created opportunities for new types of crimes to occur. Policymakers and criminal justice institutions have struggled to keep pace with technological innovation and its impact on criminality. Criminal law and justice, as well as investigative and prosecution procedures, are often outdated and ill-suited to this type of criminality as a result. While technological solutions are being developed to detect and prevent digitally-enabled crimes, generic solutions are often unable to address the needs of criminal justice professionals and policymakers. Focussing specifically on social media, this article offers an exploratory investigation of the strengths and weaknesses of the current approach used to police offensive communications online. Drawing on twenty in-depth interviews with key criminal justice professionals in the United Kingdom, the authors discuss the substantial international challenges facing those seeking to police offensive social media content. They argue for greater cooperation between policymakers, social science and technology researchers to develop workable, innovative solutions to these challenges, and greater use of evidence to inform policy and practice.
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.
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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".