On What Autoethnography Did in a Study on Student Voice Pedagogies: A Mapping of Returns
Bibliographic record
Abstract
In this paper, I invite you into some considerations of what autoethnography might do in research, what it might teach us as researchers. In doing so, I return to an autoethnographic study I engaged in a few years ago which was contoured through the question: How do teachers experience student voice pedagogies? In that study, I experienced autoethnography as a creative methodology that allowed me to go back to two experiences I had with youth, or student voice projects. The paper embodies a return to the autoethnographic study of my doctoral research, which itself was a return to the previously experienced student voice projects; a return that is being propelled by my new position as a professor, supervising students in the mappings of their research landscapes. Returning, thus, becomes a central motif that invites dwelling in the simultaneity of pastpresentfuture – wherein the present is the folding in of the past and the future through attuning to embodied ways of knowing, sensing, being, and doing -- disrupting colonial epistemological legacies of progress and linearity found in conventional and taken-for-granted research practices. I ask, what does it mean to go back, in efforts oriented towards a future (such as social justice)? What might it mean to conceptualize time differently within our research, teaching, and learning? I argue that autoethnography, when engaged through an active nomadism, opens space for learning about our research practices, ourselves as researchers and pedagogues, as well as deeper understandings of our research topics.
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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 | 0.031 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.033 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.008 |
| 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".