Learning to Teach Outside the Box: Exploring Newness in Literacies Pedagogies in a Pandemic
Bibliographic record
Abstract
This article explores the innovative lesson planning assignments of preservice teacher, Marie, as part of an alternate teaching practicum during the pandemic closure of schools in Spring 2020. Marie viewed this shift in context as an opportunity to “think outside of the box”, to be creative and divert from a traditional lesson planning template. As we read the examples from Marie’s lesson plan assignments, we think with posthumanist theories of entanglement, intra-actions and the producing of newness in literacies pedagogies. We share data that show the entanglements of more-than-humans and humans within the innovative lesson plan format. In exploring Marie’s lesson plan redesigns and her reflections on them, we consider the ways these pedagogies were produced through the intra-actions of assignment criteria, provincial curricula, Marie’s knowledge of her students, families, available learning materials, and pandemic conditions. We consider how the implications of this lesson format contribute to newness in our ways of thinking and doing as teacher educators of literacies.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".