Pandemic Open Data: Blessing or Curse?
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 SARS-CoV-2 pandemic spawned an abundance of open data originally collected by local public health agencies, then aggregated, enriched, and curated by higher-level jurisdictions as well as private corporations such as the news media. The COVID-19 datasets often contain geospatial references making them amenable to being presented cartographically as part of map-centered dashboards. Pandemic open data have been a blessing in that they enabled independent scientists and citizen researchers to verify official proclamations and published narratives related to COVID. In this chapter, however, we demonstrate that these data also are cursed with serious issues around variable definitions, data classification, and sampling methods. We illustrate how these issues interfere with unbiased public health insights and instead support narratives such as the “pandemic of the unvaccinated.” Nevertheless, open data can serve as a tool to counter dominant narratives and state-sanctioned misinformation. To advance this purpose, we need to demand disaggregated data with transparent metadata and multiple classification schemes.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.005 |
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