Five stories of accidental ethnography: turning unplanned moments in the field into data
Bibliographic record
Abstract
Observations of daily life are the bread and butter of ethnography but rarely feature as data in other kinds of work. Could non-ethnographic studies also benefit from such observations? If so, how? This article proposes ‘accidental ethnography’ as a method that field researchers can use to gain better understanding of the research context and their own social positioning within that context. Accidental ethnography involves paying systematic attention to the unplanned moments that take place outside an interview, survey, or other structured methods. In these moments the researcher might hear a surprising story or notice an everyday scene she had previously overlooked. The importance of these observations lies not in what they tell us about the particular, but rather what they suggest about the larger political and social world in which they (and the researcher) are embedded. The paper illustrates the argument by presenting five stories from the author’s experiences conducting research on local violence in Rwanda, Bosnia, the US, and elsewhere.
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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 | 0.021 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".