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 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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".