COVID-19: A case for the collection of race data in Canada and abroad
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
Racialized populations have consistently been shown to have poorer health outcomes worldwide. This pattern has become even more prominent in the wake of the coronavirus disease 2019 (COVID-19) pandemic. In countries where race disaggregated data are routinely collected, such as the United States and the United Kingdom, preliminary reports have identified that racialized populations are at a heightened risk of COVID-19 infection and mortality. Similar patterns are emerging in Canada but rely on proxy measures such as neighbourhood diversity to account for race, in the absence of person-level data. It follows that the collection of race disaggregated data in Canada is a crucial element in identifying individuals at risk of poorer COVID-19 outcomes and developing targeted public health interventions to mitigate risk among Canada's racialized populations. Given this continuing gap, advocating for timely access to this data is of great importance owing to the challenges that the COVID-19 pandemic has highlighted amongst racialized populations in Canada and worldwide.
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 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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