Learning from our Multi-Stage Collaborative Autoethnography
Bibliographic record
Abstract
This article is a reflection on eight, then seven, now five women’s collaborative efforts to explore the development of our own leader identities. While each of us conducts research on women and leadership, we are a diverse group of women: we were born in three different countries (United States, Paraguay, and New Zealand) and currently live in three different countries (United States, Canada, and New Zealand). We are of diverse races, sexual orientations, and generations; we have leadership experiences in a variety of disciplines and industries; and we vary in the priority we place on this study. In this paper, we review our experiences conducting research during the first three plus years of our collaborative autoethnographic study and share what we learned from those experiences. We address previously published considerations for developing collaborative autoethnographies including: the number of participants involved; the extent of involvement of the participants and the level of collaboration during the study; the collaborative approaches used in the study; and the approaches to writing. We add a reflection on our leadership practices throughout the study and on the confidentiality challenges that emerged. We also discuss how our division of the study into multiple life stages and multiple projects within the life stages has influenced our experiences and how the challenges resulting from the long duration of our study have influenced our productivity and are expected to influence our future plans. Our lessons learned should prove useful as other autoethnographic research groups begin their own research processes.
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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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".