The Executive Order “Restoring Gold Standard Science” is Dangerous for America
Bibliographic record
Abstract
Abstract The recent U.S. executive order “Restoring Gold Standard Science” poses a significant threat to the U.S. national economy and security. The order replaces the scientific experts who lead U.S. governmental scientific organizations with non‐scientific political appointees who would have the power to decide what science could and could not be published. In doing so, the executive order threatens to reverse more than 80 years of scientific advancements that have given the U.S. its world‐leading military, technology, and economy. The justifications provided in the executive order for this change in policy are false or misleading in their assessment and representation of the current state of U.S. scientific scholarship. Hypocritical in its aims, the executive order claims to promote integrity in science while at the same time calling to remove the “Framework for Federal Scientific Integrity Policy and Practice” that currently ensure veracity and credibility in science. The executive order is also unconstitutional, threatening to take away the First Amendment rights of scientists by punishing them if they publish truthful and accurate science that is contrary to the administration's political agenda. Such censorship of scientists has been attempted by failed governments of the past such as Nazi Germany, the Soviet Union, and early communist China, always with disastrous consequences for their citizens. “Restoring Gold Standard Science” needs to be rescinded to avoid catastrophic consequences for the U.S. economy and national security.
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".