Surrogate Ethnography: Fieldwork, the Academy, and Resisting the IRB
Bibliographic record
Abstract
Nearly every ethnographer has observed the growing reach and increasingly uncomfortable power of the IRB. As IRB restrictions have grown tighter, the consequences have become more dire. The failure of most IRBs to understand ethnography at all, along with increasing concerns about litigation that trump the welfare of both researchers and “subjects,” and the usurping of faculty power by the administration in universities, has left us with a difficult challenge: how can ethnographers and participant observers continue to do their research, without losing their jobs? This paper introduces a new methodology (“surrogate ethnography”). We posit that surrogate ethnography provides three distinct benefits: (1) it represents a methodological and ethical resistance to excessive IRB control, (2) it can help us rescue ethnography and participant observation—at least in a certain form—from IRBs, and (3) it addresses some of the longstanding concerns with autoethnography by proposing an alternative reflective analytic approach to one’s experiences in the field. We share the results of our initial foray into surrogate ethnography, offering our analyses of each other’s stories (one from volunteer work in a prison, and one from participation in sadomasochism/BDSM) in the context of constraints and challenges facing ethnographers in the current academic climate.
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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 | 0.179 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.025 | 0.046 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".