Watching the watchers: conducting ethnographic research on covert police investigation in the United Kingdom
Bibliographic record
Abstract
It has long been claimed that the police are the most visible symbol of the criminal justice system (Bittner, 1974). There is, however, a significant strand of policing – covert investigation that relies routinely on methods of deception – that resists public revelation (Ross, 2008). The growing importance of covert police investigation has profound implications for the relationship between citizen and the state in a democratic society, but it is relatively unexplored by police researchers. In this article, we describe the methodology of the first ethnographic study of how the introduction of the Regulation of Investigatory Powers Act (2000) – a piece of ‘enabling’ legislation that regulates the conditions under which law enforcement agencies can intervene in the privacy of individuals – has effected the conduct of covert police investigation in the United Kingdom. We describe our ethnographic experience in the ‘secret world’ of covert policing, which is familiar in many respects to ethnographers of uniformed officers, but which also differed significantly. We contend that the organizing principle of surveillance – the imperative to maintain the secrecy of an operation – had a marked impact on our ethnographic experience, which eroded significantly our status as non-participant observers and altered out reflexive experience by activating the ‘usefulness’ of our gender.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".