CHIP50 Report #115: American Attitudes Toward Government Interventions in Science
Bibliographic record
Abstract
• Americans who disapprove of the administration’s science-related policies outnumber those who approve by more than two to one. On average, 48% disapprove or strongly disapprove of recent government actions in that space, while only 21% approve or strongly approve. • The most negatively viewed actions are the pause in public health information dissemination (51% disapproval) and the firing of National Oceanic and Atmospheric Administration (NOAA) employees (50% disapproval). • Approval levels for individual actions are low; only the dismantling of the U.S. Agency for International Development (USAID) (29%) and the layoffs at the Centers for Disease Control and Prevention (CDC) (27%) received more than 25% approval. • Average approval of science-related government actions is highest among Republicans (42%), men (28%), graduate degree holders (30%), and high-income respondents (31%). Disapproval is strongest among Democrats (74%), African Americans (56%), women (53%), and those aged 65 and older (55%). • A majority of Americans support greater government investment in research: 57% favor increased medical research funding and 42% support increased scientific research funding. Relatively few want funding cuts: only 10% for medical research and 16% for science. • Even among Republicans, nearly half (48%) favor more medical research funding, though only 31% support increases in science research funding. A quarter of Republicans support cuts for scientific research and 15% for medical research. • While support for research remains strong, the proportion of Americans reporting high trust in scientists declined from 58% in 2020 to 36% in 2025, with sharper drops among Republicans (from 54% to 26%) than among Democrats (from 67% to 50%). • Despite declines in public confidence, scientists and doctors remain more trusted than most institutions, including Congress, the Supreme Court, and the news media.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".