Assessing public support for social policy in times of crisis: evidence from the Child Tax Credit during the COVID-19 era in the United States
Bibliographic record
Abstract
Abstract The 2021 American Rescue Plan included the temporary expansion of the Child Tax Credit (CTC)—the largest individual income tax credit program in the United States—for most families with children. In the context of the COVID-19 pandemic, how did the public perceive this social policy benefit for families, especially in relation to other traditional social programs? By focusing on the CTC, an understudied policy area, and presenting original survey data, this paper first shows that, while the majority of respondents favored the CTC, levels of support for these benefits were lower than support for other social programs. Second, the paper suggests that, compared to older people and people with disabilities, Americans view families as part of the “undeserving” population. Third, by presenting panel data, we show that there is no change in levels of CTC support even among recipients of these benefits. Overall, these findings shed light on important challenges to the development and implementation of family policy in the USA, as well as the possibility of recalibrating the US liberal welfare state.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".