The Community School Initiative in Toronto: Mitigating Opportunity Gaps in the Jane and Finch Community in the Wake of COVID-19
Bibliographic record
Abstract
COVID-19 significantly impacted the delivery of education with widespread disruptions, particularly disadvantaging racialized and low-income families. Our research project explored how community-based programming can be adapted and mobilized to mitigate opportunity and achievement gaps for Black, Indigenous, people of colour (BIPOC), and those from lower socio-economic backgrounds. The project as a case study examined an afternoon and weekend supplementary academic program called the Community School Initiative (CSI), offered from September 2020 to May 2021 to members of the Jane and Finch community in Toronto, Canada at a subsidized cost. CSI is a partnership between the non-profit organization Youth Association for Academics, Athletics, and Character Education (YAAACE) and the for-profit enterprise Spirit of Math. It delivers a structured math curriculum to students in grades two to eight aged 8 to 14 years, old supported by a team of caring adults including parents, coaches, and Ontario certified teachers. The efficacy and outcomes of the CSI was assessed through surveys with parents (n=33), students (n=33), and teachers (n=4), and a focus group with seven teachers delivering the curriculum in the CSI. We also discuss the significance of how the research was conducted in the wake of COVID-19. Hence, this article is about the findings from the data, but just as much about the community-driven approach to how the research was conducted, by the community and for the community.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".