Bibliographic record
Abstract
As we write this editorial in December 2022, a season of celebration, reflection, and renewal, we are struck by how each article in this winter issue offers something to celebrate about Canadian public schools, while simultaneously urging us to reflect on what needs to be changed and improved.Cutting-edge research in this issue offers much to educators, leaders, and policy makers for planning ahead.Specifically, covering the period of the last two decades, including the COVID-19 pandemic, the research in this collection illuminates learning opportunities, experiences, and outcomes across primary and secondary schools in Canada and the provincial-level policies that determine and/ or shape them.The specific topics include high school completion patterns, the effects of summer learning programs, the experiences of physically-distanced learning during the pandemic, cross-country policy responses to the pandemic, and the future of robotics-incorporated education.In this editorial, we discuss the significance of each of these studies, while emphasizing that more research is needed on issues impacting under-represented groups, especially research undertaken by, with, and for Indigenous and Black people and communities.Robson, Malette, Anisef, Maier, and Brown investigate persistent questions about why some students complete high school while others do not.By drawing on data from two Grade 9 cohorts ( 2006 and 2011) from the Toronto District School Board, their research contributes to understanding the patterns of high school completion in Canada's largest city and draws on demographic data (gender, race, parental education, and household income) and school-related predictors, such as academic achievement, special
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.067 | 0.034 |
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".