When Crime Is a “Young Man’s Game” and the Ethnographer Is a Woman: Gendered Researcher Experiences in Two Different Contexts
Bibliographic record
Abstract
Ethnographers have long explored the challenges of gender dynamics in researcher–participant relationships, particularly in relation to attempts by female researchers to gain and maintain access to male research populations. However, little is known about these relationships in urban research settings characterized by crime and violence, where gender relations between young men and women are shaped by often extreme forms of social marginalization. Drawing on the field experiences of two female ethnographers who studied disadvantaged and criminalized groups of men in Germany and Canada, our article sheds light on how our experiences in our respective research sites were molded by the local contexts where our ethnographies took place. In particular, we analyze how the respective cultural meanings that the men subscribed to affected their perceptions of women, and how these perceptions ultimately shaped our interactions with our research groups, structured our gendered experiences, and presented quite different challenges for us as female “crime” ethnographers.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.027 | 0.036 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".