Bibliographic record
Abstract
Autoethnography is an avant-garde method of qualitative inquiry that has captured the attention of an ever-increasing number of scholars from a variety of disciplines. Personal experience methods can offer a new and unique vantage point from which to make a contribution to social science yet, autoethnography has been criticized for being self-indulgent, narcissistic, introspective, and individualized. Methodological discussions about this method are polarized. As an autoethnographer and qualitative methodologist with an interest in personal experience methods, I have had the opportunity to review several autoethnographic manuscripts over the years. As my reviews accumulated, I began to see themes in my responses and it became apparent that I was advocating for an approach to autoethnography that lies in contrast to the frequently offered methodological polemics from philosophically divergent scholars. In this article, I draw from the reviews I have done to address topics such as applications and purposes for autoethnography, the degree of theory and analysis used within the method, data sources and dissemination of findings, and ethical issues. I then connect the concerns I see in the reviewed manuscripts to examples in the autoethnographic literature. Ultimately, I propose a moderate and balanced treatment of autoethnography that allows for innovation, imagination, and the representation of a range of voices in qualitative inquiry while also sustaining confidence in the quality, rigor, and usefulness of academic research.
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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 | 0.157 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".