Bibliographic record
Abstract
The COVID-19 pandemic had immediate and potentially long-lasting impacts on cities. Yet, the ability to assess, monitor, and analyze the wide-ranging effects of the pandemic has been stymied by data challenges. The pandemic elevated the need for, and reliance on, a wide range of data sources. We discuss four data challenges related to understanding the impact of the pandemic on cities. First, we explore how shifts in public policy and the decisions of private companies altered data collection priorities, availability, and reliability. Second, we discuss temporal dimensions, including the speed of data retrieval and frequency of data collection. Third, we identify the growing use of unexpected sources, which often feature a lack of rigor and consistency. Fourth, we explore the spatial scale of study and highlight questions about the interpretation of boundaries constituting the city. We use examples from the City of Toronto to ground our observations while also pointing to broader issues. We note that the tension between rapid, novel data and slow, consistent data continues to evolve and argue that a deeper appreciation and analysis of, and access to, myriad sources of data are necessary to understand the immediate and long-term impacts of COVID-19 on cities. Beyond the pandemic, our essay contributes to ongoing and emerging debates regarding the use of big data to understand the challenges facing cities and society.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".