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
Vaccine equity holds the key to ending the coronavirus disease 2019 (COVID-19) pandemic. Yet most prior work on vaccine equity has compared vaccine uptake across neighborhoods with varying sociodemographic composition or assessed whether vaccine disparity across neighborhoods has diminished over time. Researchers seldom examine the extent to which vaccination helps reduce inequalities in the prevalence of COVID-19 across neighborhoods. Using administrative data from the City of Toronto, the authors compare the vaccine trajectories of neighborhoods with low, moderate, and high COVID-19 rates. The authors also examine whether disparities in COVID-19 rates have narrowed or widened as vaccinations have become more available. By mid-June 2021, differences in vaccination rates by neighborhoods’ COVID-19 levels were small, but disparities in COVID-19 rates across neighborhoods persisted. Equality in vaccination rates is not a silver bullet to reducing inequalities in COVID-19 infections across neighborhoods with varying sociodemographic characteristics and likely variations in exposure to the COVID-19 virus.
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 | 0.006 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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