Bibliographic record
Abstract
Four days before the 2022 State of the Union address, the CDC made a change to the risk-prevention pandemic map of the United States that changed the color of the map from red to green. Before the change, the CDC worked with guidelines that identified a substantially high risk as 50 cases per 100,000 people. After the change, the low risk category became 200 cases per 100,000 - a significant diminution of the calculation. As the People’s CDC notes, “The resulting shift from a red map to a green one reflected no real reduction in transmission risk. It was a resort to rhetoric: an effort to craft a success story that would explain away hundreds of thousands of preventable deaths and the continued threat the virus poses.” The People’s CDC, a collaborative of volunteer health experts who offer policy recommendations and guidance around COVID-19 issues, reframes the official US responses to the global health crisis by disseminating critical information via social media. On their website People’s CDC identify their collaborative project: “working alongside community organizations, we are building collective power and centering equity as we work together to end the pandemic.” In this presentation, I situate the equity-focused work of the People’s CDC within restorative justice and world building movements. Using their “layered, collective, and equitable” social media publications as a guide, I employ recent research on collective action to analyze the ways that the People’s CDC resists the hegemonic inequities embedded in state run public health systems and corporate mass media by redeploying and rewriting the rhetorical strategies of those systems. I argue that the People’s CDC’s ethos of care and collaboration is employing restorative world building practices as a response to global crisis. As they say, “We must urgently create a new normal in which commitment to community care drives policy and behavior. This charge requires sustained and collective action.”
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.034 | 0.067 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".