Crowdsourcing Criminology: Social Media and Citizen Policing in Missing Person Cases
Bibliographic record
Abstract
Criminology is undergoing a process of innovation and experimentation with the rise of social media. Although police have traditionally been the locus of legal enforcement, ordinary citizens are increasingly afforded opportunities to participate in crowdsourced investigations. In this article, we explore the emerging field of crowdsourcing criminology and its relationship to newsmaking criminology, public criminology, and the reshaping of news as infotainment (popular criminology). Drawing on a case study of a missing person named Emma Fillipoff, and our experience of involvement in the development of a television (TV) documentary dedicated to help finding Emma, we examine the process of crowdsourcing in practice and how it may oscillate between infotainment and public criminology inspired by academic evidence. Crowdsourcing criminology represents both a theoretical and an applied shift in our research focus and paves the way for a host of new projects that strive to reveal the strategies and techniques that define and characterize crowdsourced investigations.
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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 | 0.027 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".