COVID-19, the New Urban Crisis, and Cities: How COVID-19 Compounds the Influence of Economic Segregation and Inequality on Metropolitan Economic Performance
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
This paper examines the connection between measures of a U.S. metropolitan area's new urban crisis (i.e., unaffordable housing, economic inequality, and residential segregation) and its year-over-year employment change in the period immediately before and during the COVID-19 pandemic. Results show that measures of the new urban crisis did not generally have a statistically significant association with year-over-year employment change between January and September of 2020, which captures the period before COVID-19 and the beginning of the pandemic (e.g., shutdown). The severity of a region's economic segregation and inequality, however, are associated with higher rates of employment decline in the early recovery months of October to December of 2020. These findings suggest that places that rate worse for indicators of the new urban crisis were less able to recover from the negative economic shocks related to COVID-19.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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