Bibliographic record
Abstract
Emerging forms of surveillance and policing make use of social media platforms like Facebook and Twitter. This paper considers top-down conventional policing by investigative agencies, as well as ground-up policing by crowd-sourced users. These practices have separate origins and organisational cultures, yet they now converge on platforms that increasingly monopolise social life. While bottom-up policing contains empowering potential, notably by shedding light on instances of police misconduct and political corruption, so to can it be directed towards categories of individuals that are suspected of criminal activity and breaching social norms. Furthermore, the emergence of top-down scrutiny of social media platforms by police suggest that institutions and governments are as capable as ever of asserting control over social life. Three examples are considered as indicative of police presence and other forms of policing on social media: the emergence of technologies and services that enable police to perform top-down surveillance of social media platforms, bottom-up informal policing on social media following the 2011 Vancouver riot, and market-based attempts to crowd-source user-led surveillance on digital media. Ground-up and topdown forms of policing do not exist independently. Rather, they interact with and influence one another. Policing by the public suggests that although digital media allows for counter-power, so too does it allow a ground-up manifestation of state control in the form of law and order politics, including profiling and discrimination. And while users and members of the public may be willing participants in police work, so too are they unwilling participants when their personal information is repurposed as evidence by law enforcement agencies.
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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".