Downtown Recovery and Polycentricity Quotients for US and Canadian Downtowns
Bibliographic record
Abstract
This dataset agglomerates calculated recovery and polycentricity quotients for 62 downtowns in the United States and Canada, used for the paper: "Can We Save The Downtown? Examining Pandemic Recovery and Polycentricity Trajectories across 62 North American Cities" by Leong, Huang, Moore, Chapple, et al. Variables reflect calculations from Patterns data by SafeGraph, Inc. The calculated metrics include: Recovery Quotient (Downtown): Proportion of visits from mobile devices detected in the downtown area (specified by zip codes) in the specified week compared to the corresponding week in 2019. Recovery Quotient (City): Proportion of visits from mobile devices detected in the entire city (specified by zip codes) in the specified week compared to the corresponding week in 2019. Location Quotient: Ratio of the Recovery Quotient (Downtown) to the Recovery Quotient (City), reflecting the relative recovery of the downtown and the city.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".