Gwendolyn L. Wright, Lucas Hubbard, and Darity William A., Jr. eds. <i>The Pandemic Divide: How COVID Increased Inequality in America</i>
Bibliographic record
Abstract
This anthology of 11 short essays, bracketed by an excellent introduction and a punchy conclusion, is based on a wealth of scholarship and statistics that use the COVID-19 pandemic as a lens to expose the enormous racial disparities in American society—disparities that were simply magnified by the challenges and tragedies of the crisis. While the authors acknowledge race is a social construct with no firm biological definition, they proclaim its powerful impact on all aspects of existence: health care, education, employment, finances and even leisure. Only one of the authors is identified as a historian (Joe William Trotter, the Giant Eagle University Professor of History and Social Justice at Carnegie Mellon University); his essay on labour history shows how the racialised population was predisposed to greater harm through the social determinants of health. Most of the 27 other authors are experts in public policy with backgrounds in sociology, education, psychology finances, anthropology, and health care. Nevertheless, they open their specific chapters with historical narratives that pre-date the pandemic often by many years going back to slavery—narratives that illustrate how the greater impact of COVID-19 on racialised communities was a predictable product of structural racism in the USA.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.082 | 0.045 |
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".