Bibliographic record
Abstract
Autoethnography as an approach to inquiry has gained a widespread following in part because it addresses the ethical issue of representing, speaking for, or appropriating the voice of others. In this article, I place the emergence of autoethnography within its historical context and discuss the contributions and limitations of autoethnography as an approach to inquiry. I examine ethical aspects of autoethnography, showing how the method is rooted in ethical intent, yet autoethnographers nevertheless face ethical challenges. I suggest that collaborative autoethnography, a multivocal approach in which two or more researchers work together to share personal stories and interpret the pooled autoethnographic data, builds upon and extends the reach of autoethnography and addresses some of its methodological and ethical issues. In particular, collaborative autoethnography supports a shift from individual to collective agency, thereby offering a path toward personally engaging, nonexploitative, accessible research that makes a difference.
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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 | 0.126 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.077 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 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".