(Un)Seeing as Care or Control: The Collection of Race-Identified COVID-19 Data
Bibliographic record
Abstract
The over-surveillance of racialized or colonized groups for the purposes of control is a well-documented issue. At the same time, there tends to be an under-monitoring of these same groups in cases where such surveillance by state or governmental actors could have implications for care outcomes through the safeguarding of public health provisions. This article draws attention to calls in Canada, particularly by black communities, for the collection of race-identified COVID-19 patient data. The collection of such race-identified data has been deemed by proponents as necessary for a more thorough understanding of and equitable policy response to the pandemic. While these calls mean making an already over-surveilled population more visible to states and governments, they also represent an exercise of agency by members of oppressed groups in negotiating how and when they should be visible. Such calls for race-identified data thus unsettle the increasingly “negative” understanding of surveillance and highlight how the care potential of surveillance cannot be dismissed even if surveillance systems are simultaneously dangerous.
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".