Navigating the Brick Wall: School Settlement Workers’ Responses to Exacerbated Inequities for Newcomer Students in COVID-19
Bibliographic record
Abstract
For newcomer students, inequities exacerbated by COVID-19, including racism, unfold within their educational landscapes. School settlement workers perform a critical role in newcomer students’ educational trajectories. COVID-19 has intensified the importance of school settlement work, yet school settlement workers remain an under-researched and under-recognized group of professionals. Anchored in an anti-racist, multidisciplinary framework, our article traces how COVID-19 measures in schools have magnified inequities for school settlement workers and newcomer students. Our data, drawn from a community-based project, consist of virtual focus groups held with school settlement workers located in a Canadian prairie province during the height of the pandemic, and the findings emerge through a critical approach to the methodology of appreciative inquiry. We illuminate systemic realities to contradict discourses that the pandemic does not discriminate and demonstrate how COVID-19 protocols are used to justify and obfuscate schooling exclusions along racial lines. We analyze themes of (in)visibility of settlement work, whiteness and racism, and resistance through Sara Ahmed’s (2012, 2017) metaphor of the brick wall to animate the tensions of settlement work in schools during COVID-19. We conclude with school settlement workers’ recommendations to increase recognition of their critical role and to support their work during and beyond the pandemic. We call on institutional wall makers to respond to settlement workers’ recommendations and actualize institutional commitments to newcomer students and families.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.043 | 0.035 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".