Choice With(out) Equity? Family Decisions of Child Return to Urban Schools in Pandemic
Bibliographic record
Abstract
In response to the COVID-19 global pandemic, most schools across the country closed in-person instruction for a period of time and many shifted to online schooling. Beginning in fall 2020, schools around the United States began reopening and many districts offered families a decision or “choice” to return their children to an in-person or online schooling experience. In many cities, this approach complicated existing school choice and permanent closure policies with already existing equity issues. Building upon previous scholarship on school choice and closure, this study draws on the concept of school choice with(out) equity (Frankenberg et al., 2010; Scott & Stuart Wells, 2013; Horsford et al., 2019). Using data from an online survey (n = 155 participants) in August 2020, this study examines why families (50% white, 50% people of color) decided to return their children to in-person schooling in Hartford, Connecticut. This study uses a mixed-method analysis of qualitative responses and quantitative data to understand family decisions to return to in-person schooling (Creswell, 2014). Rather than school choices with full equity considerations during the pandemic, these family responses focused on needs of childcare for full-time work and health safety. These responses suggest a partial equity in the landscape of available choices. The study raises questions about reapplying old forms of school choice to a new form of temporary school closure during pandemic.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".