Tales from the field: reflections on four decades of ethnography
Bibliographic record
Abstract
Drawing on careers spanning over 35 years in the field of ethnography, we reflect on the research in which we’ve engaged and how the practice and epistemology of ethnography has evolved over this period. We begin by addressing the problematic nature of ethical issues in conducting qualitative research, highlighting the non-uniform nature of standards, the difficulty of applying mainstream or medical criteria to field research, and the issues raised by the new area of cyber research, drawing particularly on our recent cyberethnography of self-injury. We then discuss the challenge of engagement, highlighting pulls that draw ethnographers between the ideals of involvement and objectivity. Finally, we address the challenges and changing landscapes of qualitative analysis, and how its practice and legitimation are impacted by contemporary trends in sociology. We conclude by discussing how epistemological decisions in the field of qualitative research are framed in political, ethical, and disciplinary struggles over disciplinary hegemony.
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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 | 0.083 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.033 | 0.051 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.006 | 0.029 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".