An Early Childhood Educator’s Learning Story in the Time of COVID
Bibliographic record
Abstract
While it began with a variety of narrative representations of writing personal experiences, since Ellis (2004; Bochner & Ellis, 2016), evocative, performative, and creative nonfiction forms of storying have coalesced to form contemporary autoethnography. For over a decade, Canadian arts education researchers have blazed trails to employ those forms of autoethnography as “learning stories” (Carr, 2001; Carr & Lee, 2012) to study teaching and learning practices in a variety of school and community educational contexts. Learning stories enable educators to reveal teaching and learning experiences that cannot be represented by, or communicated through, other research forms. The present inquiry, which begins with the story of an early childhood educator, is rooted in the fusion of evocative autoethnography and learning stories with arts-based research, particularly a/r/tography.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".