Tracing COVID-19 Data: Data and Technological Citizenship during the COVID-19 Pandemic
Bibliographic record
Abstract
Project Overview / Research Summary: There is much official COVID-19 data reporting by federal, provincial, territorial and Indigenous Communities. As the pandemic evolves, and more information comes to light, there is a call to add data attributes about Indigenous, Black and Racialized groups and of the affected labour force, and to report where cases predominate. The pandemic also revealed that foundational datasets are missing, such as a national list of elder care homes, maps of local health regions and data about the digital divide. This project will embrace technological citizenship, adopt a critical data studies theoretical framework and a data humanitarian approach to rapidly assess data shortfalls, identify standards, and support the building of infrastructure. This involves training students, conducting rapid response research, developing a network of experts, learning by doing and a transdisciplinary team of peer reviewers to assess results. The knowledge will be mobilized in open access blog posts, infographics, policy briefs and scholarly publications.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".