Downtown Recovery and Polycentricity Quotients for US and Canadian Downtowns
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 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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