Accidental ethnography: A method for practitioner-based education research
Bibliographic record
Abstract
This article presents and discusses Accidental Ethnography (AccE), a methodology for practitioners to examine past experiences and contribute their findings to scholarly discourse. Accidental ethnography is the systematic analysis of prior fieldwork. It utilizes extant data “accidentally” gathered (i.e. the data were not collected as part of a predesigned study) to provide insight into a phenomenon, culture, or way of life. The accidental ethnography method—a nascent method in research literature—was developed to provide a means of in-depth exploration of past practitioner learning experiences beyond personal reflection. This article organizes, advances, and systematizes an accidental ethnography method for practitioner–researchers. We propose here a method that encompasses broader intentionality on the part of the researcher and a potentially unorthodox chronology of steps in the ethnographic research process. For practitioners in education, where much is learned through action and reflection, accidental ethnography offers a methodological approach for rigorous reflective research by front-line practitioners who have traditionally had difficulty finding time to make rigorous contributions to the discipline. This article introduces the methodological approach, elaborates the accidental ethnography research process, situates the method within action research methodology, and provides an example of an accidental ethnography project.
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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 | 0.092 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".