Household curricula during the <scp>COVID</scp>‐19 pandemic: A collective biography
Bibliographic record
Abstract
Abstract Households with school‐aged children worldwide were affected by school closures caused by COVID‐19. Using a sociomaterial orientation and collective biography methodology, this study examined the household curricula of diverse families in Ontario, Canada with children in pre‐school through Grade 12. It found two distinct curricular phases to the pandemic, each with its own networked constituents, movements, and effects. Phase I involved learning at home during the lockdown in Spring and Summer 2020; Phase II involved online and face‐to‐face learning in the Fall of 2020. The constituents involved in curriculum making in Phase I were expansive and unexpected. Multiple timescales, modes, languages, and knowledge disciplines assembled to (re)configure households as learning spaces that produced novel opportunities for children's knowing, doing, and being. The makeup and movements of the Phase II assemblages were more of a return to the normalized boundaries of implemented school curricula that demarcated subject areas, languages, learning/play, learning/assessment, and body/mind. Concerned with questions of equity in/through curriculum, this study suggests a curriculum paradigm that foregrounds learners' and teachers' engagement with sociomaterial lifeworlds and their ethical relationship building with the more‐than‐human and the world.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".