What a highly controversial ethnography says about tensions, problematizations and inequality in contemporary ethnographic practice and regulation
Bibliographic record
Abstract
Purpose The purpose of this paper is to illuminate how inequality – in the way ethnography as a research tool itself is used – underwrites many of the methodological tensions in the recently published and widely-debatedOn the Run: Fugitive Life in an American Cityby Alice Goffman. Design/methodology/approach The author conducts an in-depth, critical analysis ofOn the Runas an epistemological case to visualize methodological and moral challenges that burden ethnographic practice at large. Findings The author opens dialogue on undercover ethnography, the overreach of institutional review boards, privilege in the use of ethnography as a research tool, “Othering” and the exoticization of the underclass, and the boundary shift from observer to participant roles with deep immersion. The author unpacks these areas of contention toward the construction of a potential alternative combining public sociology with what is called a sociology of compassion. Originality/value While the book provides an intimate, rich account of the experience of law among the underclass, the author demonstrates that it constitutes an epistemological case ideal for examining how the issues of pre-fieldwork preparation, positionality and deep immersion are conceived – and problematized – in mainstream ethnographic practice.
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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 | 0.041 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.124 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".