A Main Street framework leveraging data and technology for good
Bibliographic record
Abstract
The COVID-19 pandemic has hollowed out corporate office spaces in large US metropolitan centers, resulting in three potential downstream differential impacts: (1) on places, as demand for urban office spaces, commercial real estate, and housing have changed; (2) on profits, as small and local businesses in proximity to these office spaces depend on office workers and other foot traffic; and, (3) on people, as the livelihoods of many diverse but historically marginalized communities have been disproportionately affected. In this article, we examine these impacts, with downtown Seattle used as a case study to validate some urban trends. In leveraging data and technology-based approaches to assess and support urban vitality and equity goals, policymakers can explore the value of a Main Street data-driven analytical framework. Here, we explore how such a framework can support more targeted responses, including implementing technology policy initiatives that increase the digitalization of Main Street businesses and support their resilience. Complementing this data-driven framework, institutionalizing equity analysis in regional decision-making systems can better account for differential impacts on vulnerable communities to implement more inclusive future of work recovery strategies.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".