A collaborative autoethnographic journey of collective storying: Transitioning between the ‘I’, the ‘We’ and the ‘They’
Bibliographic record
Abstract
The story we share here is about lessons learned during a three-year, collaborative autoethnographic journey beginning in January 2020. Our story is one of conducting a meaningful inquiry into our shared lived experience amid the changes brought about by COVID-19 lockdowns. Our insights speak to how we collaboratively reflected and researched across institutions, countries, disciplines, and career stages. More importantly, in making our process explicit, we highlight the way storying was experienced within our collective space. In doing so, we explore insights about how stories are adapted and transformed through a process of navigating the development of, and transitions between, pre-public and public spaces. Using an Arendtian lens, we explore the question, How are autoethnographic collaborative stories crafted for research in an academic context? Our insights present a cyclical and developmental frame within which to process collaborative storying and indeed collaborative academic work.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.024 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".