Early Childhood Education in Canada During a Pandemic
Bibliographic record
Abstract
In Fall 2020, we circulated a call for papers for a special issue, "COVID and Beyond: Reconfiguring Early Childhood Education in Canada for the 21 st Century." In seeking submissions, we noticed that the COVID virus had thrown the issue of childcare, as well as the fragility of the early childhood education system, into sharp relief.We sought theoretical and/or empirical papers that reflected on the current moment and imagined a reconfigured future.We were met with an enthusiastic response from the field, as authors proposed articles from research and ideas from practice.The seven articles assembled in this issue suggest the breadth of the myriad ways the pandemic touched the field.Collectively, the papers address curriculum, policy, licensing, Indigeneity, families, children, and more.Read as an assemblage, they illuminate the wide range of complex and varying effects the pandemic has generated across Canada.Martha Friendly, Barry Forer, Rachel Vickerson, and Sophia Mohamed provide a detailed inventory of COVID and childcare in Canada, tracking a "tale of ten provinces and three territories." They undertook the valuable work of documenting closures, shifting policies, and the sudden pivots that occurred from coast to coast to coast to manage the public health needs of children, staff, and families while also serving essential workers.Among other important lessons, the detailed review makes plain the varying capacities of different jurisdictions, a function of their diverse policy architectures.Quebec's more mature childcare policy framework meant its response differed from the rest of Canada.Sophie Matthieu unpacks how policy paradigms developed and shifted through the first and second waves of the pandemic.Since English-readers have little access to the details of Quebec, this paper does much to explain Canada's (and North America's) most developed childcare system.Equally compelling, Brooke Richardson, Alana Powell, and Rachel Langford use a gender lens to critique policy responses, showing deep connections between the needs of mothers, children, and early childhood educators.In a field that has, for strategic and other reasons, sometimes sought to downplay the role of gendered stratification, this paper reasserts the necessity of a gendered lens.Alongside systemic policy at the macro level, scholars addressed how the pandemic affected relations with and between educators and children in their everyday experiences in early childhood settings.Marie-Anne Hudson and Lori Huston advocate for inclusion of diverse ways of knowing, doing, and being in early childhood environments in order for the early childhood field to authentically be "all in this together." Also calling for inclusion, Kathryn Underwood, Tricia van Rhijn, Alice-
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".