We go farther together: practical steps towards conducting a collaborative autoethnographic study.
Bibliographic record
Abstract
ABSTRACT: Autoethnography is an underused qualitative research method in implementation science. Autoethnography can be used to reflect on and archive personal experiences, which can yield useful information to advance our knowledge. In particular, collaborative autoethnography is an important method towards providing greater insights on the experiences of multidisciplinary teams conducting research amidst complexity and intersectionality. In conducting a collaborative autoethnography, all authors are participants who narrate, analyze and theorize about their individual and or collective experiences. This article provides an overview of collaborative autoethnography for health research teams and implementation scientists embarking on autoethnographic studies.
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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 | 0.208 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.012 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".