Coming Back Better: Leveraging Crisis-Response Task Forces to Advance Racial Equity and Worker Justice
Bibliographic record
Abstract
As the United States enters its third year of navigating the global Covid-19 pandemic, the coronavirus continues to disrupt the lives of millions of workers and their families. About a quarter of the US workforce—nearly 41 million workers -- experienced at least one spell of unemployment due to the coronavirus. As of February 2022, some 3 million fewer people are employed than before the pandemic. While nearly all workers have been affected, yet these impacts are highly unequal: low-wage workers, Black workers, and other workers of color, particularly women of color, have experienced the greatest health and economic harms. This lop-sided labor market recovery has done little to buoy low-wage workers of color who continue to face heavy burdens in terms of rent debt and childcare access.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.051 | 0.014 |
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".