Bibliographic record
Abstract
This brief is based on the following published work:Huey, L., & Ferguson, L. (2024). ‘No one wants to end up on YouTube’: Sousveillance and ‘cop-baiting’ in Canadian policing. Policing and Society. https://doi.org/10.1080/10439463.2024.2329239APB The political landscape has shifted, and police are increasingly the target of activist actions aimed at discrediting the institution and its members. Police must be more proactive in managing communications during political and other events. External communications – particularly monitoring and responding to social media posts across multiple platforms – should be a key component of incident and public order command and not an afterthought. Combating misinformation requires faster and/or real-time information release when possible, including photographs, live streaming, body-worn and dash camera footage, and other videos from events.
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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".