Introduction: The Promises and Challenges of Crime Ethnographies
Bibliographic record
Abstract
Abstract This chapter outlines some of the scholarly and political appeals of crime ethnographies and identifies a series of factors that will pose challenges to this methodological approach over the longer term. It briefly charts the early evolution of crime ethnographies, noting how they have expanded to encompass the study of a larger range of criminal or deviant behaviors, while also focusing on the operation of criminal justice institutions. A more diverse group of scholars than was historically the case now conduct such research, individuals who typically embrace a more reflexive orientation to knowledge production than is characteristic of positivist science. Crime ethnographies provide invaluable grounded insights into the lives of participants and processes that are often otherwise hidden or hard to reach. Politically, ethnographies tend to humanize individuals and groups that are easily vilified, while reminding politicians and officials of the need to be conscious of local variability when adopting policy initiatives that originated in different contexts. Notwithstanding the many benefits of this approach, a series of developments now present challenges to crime ethnographies as they are currently practiced, including the changing technological profile of crime, as well as university-based developments, such as changes to the systems for overseeing and rewarding academic work and research ethics protocols that do not accord with the philosophical assumptions of ethnographers or the practical realties of ethnographic fieldwork.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".