Leveraging identity to overcome temporal and financial limitations in rapid ethnography in criminological research
Bibliographic record
Abstract
With limited time and funding, scholars who deploy qualitative methodologies to examine deviance and criminogenic contexts, such as ethnography, must leverage sources of capital which reduce time-arcs and costs needed for qualitative research. Traditional ethnographic projects require both significant time and funding; accordingly, several authors have indicated the utility of “rapid ethnographies”, which require less time in the field and funding. By reflecting on three rapid ethnographies, we show how identity is simultaneously a property that informs how research unfolds and a capital that can be leveraged to compensate for temporal and financial deficits. In short, we show that rapid ethnography can be conducted ethically and that identity can counterbalance deficits in monetary and temporal capital when identity is carefully considered in the pre-planning and execution of a rapid ethnographic project.
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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 | 0.226 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.013 | 0.034 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.003 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".