Bibliographic record
Abstract
The 2019 coronavirus disease (COVID-19) cases that are being confirmed in Canada provide an opportunity to expand the epidemic model for the simulation of disease infection spread: Susceptible- Exposed-Infectious-Recovered (SEIR). This paper develops a SEIRCRT |ˈsəːkrɪt | model that integrates the Institute for Disease Modeling’s SEIR model and Critical Race Theory (CRT) to answer the question: What is in a SEIRCRT model? SEIRCRT provides a basic modeling structure from a CRT lens to simulate, predict and forecast COVID-19 cases, comorbidities affecting African Canadians, and deaths through predictive modeling. Knowledge of SEIRCRT is critical to characterize the severity of COVID-19 in this early stage. To this end, the purpose of this paper is to describe SEIRCRT’s model and summarize its key characteristics. SEIRCRT as a public health framework provides insight into the conclusions drawn about race and COVID-19, and expands our thinking about what health disparities mean for African Canadian communities.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".