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
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.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it