Neighborhood Isolation during the COVID-19 Pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The COVID-19 pandemic has disrupted Americans’ daily mobility, which could contribute to greater social stratification. Relying on SafeGraph cell phone movement data from 2019 and 2020, we use two indices proposed by Phillips and colleagues (2019) to measure mobility inequality between census tracts in the 25 largest U.S. cities. These measures capture the importance of hubs and neighborhood isolation in a network. In the earliest phases of the pandemic, neighborhood isolation rapidly increased, and the importance of downtown central business districts declined. Mobility hubs generally regained their importance, whereas neighborhood isolation remained elevated and increased again during the latter half of 2020. Linear regression models with city and week fixed effects find that new COVID-19 cases are positively associated with neighborhood isolation changes a week later. Additionally, places with larger populations, more public transportation use, and greater racial and ethnic segregation had larger increases in neighborhood isolation during 2020.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it