Leveraging identity to overcome temporal and financial limitations in rapid ethnography in criminological research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it