The Current State of School Attendance Research and Data in Canada
Bibliographic record
Abstract
The issue of school absenteeism has received increased attention in previous years due to the widespread absences caused by the COVID-19 pandemic. However, Canadian research is sparse on the topic, and a data-based picture of the extent of the problem does not exist. In this conceptual article, we briefly trace the origins of school absenteeism and outline the current status of prevalence data and research in the area of school absenteeism in Canada, drawing on a broad range of sources including national and international surveys. Our exploration suggests several recommendations to advance knowledge and practice in the area, including the identification and sharing of attendance-related data within and across provinces and territories, the development of partnerships between researchers and school boards, the integration of discipline-specific research in the area and the analysis of school absenteeism through nuanced, complex lenses.
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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 | 0.022 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.040 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".