Pandemic Schooling and the Politics of Safety
Bibliographic record
Abstract
In this paper, the authors consider how pandemic schooling is increasing educational inequalities that may have generational effects. Reviewing emerging evidence on rates of return to in-person schooling and disparities in remote learning in Ontario, the authors argue that the pandemic is accelerating existing trends of privatization and choice. Emerging data suggests that students from more affluent and whiter households have returned to in-person learning at higher rates than their lower-income and racialized peers. When families with more resources have opted for remote learning, they have been better able to supplement online lessons. For numerous reasons, including higher rates of local COVID-19 spread, fears of infection in multi-generational households, and mistrust in public schools, racialized and less affluent families are opting for remote learning at higher rates without the necessary supports. This paper situates these constrained family “choices” in a longer trajectory of school safety laws and policies that have borne down unevenly on racialized students, poorer students, and students with disabilities. School safety mandates in Ontario and elsewhere have transformed modern student life through zerotolerance discipline policies, enhanced school surveillance, and the rapid expansion of school resource officer (SRO) programs. In so doing, these laws and policies have often worked to under-protect and over-punish the most vulnerable students. The authors argue that in a time of pandemic and racial reckoning, families are making decisions about schooling in the shadow of this history.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".