Mourning Septembers: A micro poetic-narrative autoethnography of teaching planners
Bibliographic record
Abstract
I was recently very moved while reading ‘Constructing identity by writing roots into life: A poetic autoethnography’ by Andrew J. Garbisch in which he used a poetic-narrative autoethnography to explore his lived experience as a transracial Asian American adoptee. In it, he shares four of his original poems, following each with a narrative reflection. Favourite lines include the conclusion of his poem, ‘Allegory of the Tsohg’: ‘But you’re not supposed to hear any of this, I should really hush,/ Otherworldly, I’m sorry, I’ve already said too much’. Although I feel largely distanced from much of what is discussed in this piece, including adoption and experiencing the world as a person of colour, I was nevertheless struck by this project and many moments resonated, especially how his efforts to ‘construct meaning of [his] own identity’ was a somewhat ‘haunting endeavor’ (39). He inspired me to try and write a piece that ‘take[s] a bird’s eye view’ (43) of my educator journey and self – that is, how I am wrangling with reconciling that my years in academia have now eclipsed my previous time spent as a secondary English teacher. Because I have found arts-based research methods, such as narrative and poetic inquiry, to be quite generative (see, e.g. Author 2022, 2020, 2019, 2018a, 2018b), I immediately understood Garbisch’s piece as an inspiring mentor text – an exemplar of sorts for how to write to wrestle with the (albeit completely differently) somewhat haunting identity work that I find myself moving through presently. I love his bringing together of methodologies that I have used independent of one another but had not yet melded before. As such, his approach, structure and exercise in vulnerable arts-based work largely inspired this ‘micro’, snapshot-style project, which is also built on my learning from arts-based researchers, poets and storytellers I admire (e.g. Clandinin and Connelly 2000; James 2009, 2017; Sameshima et al. 2017; Faulkner 2019; Prendergast et al. 2009, among others). They have taught me a great deal, including how poetic inquiry can be a way of living in the world (Leggo cited in Irwin et al. 2019) and that narrative inquiry might consist of telling stories from our past that lead to possibilities of retellings and potential futures (Clandinin and Connelly 2000); such teachings also deeply inform this piece.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".