Bibliographic record
Abstract
EDITORIAL Stand strong for science -support our American colleaguesWe live in very challenging times, environmentally and politically.Climate change is the number one environmental threat that is affecting our lives in Canada annually, with heat waves, wildfires, hurricanes, floods and tornadoes.This general environmental uncertainty also affects us globally.Tragically, due south of us, the world is being turned upside down with the irrational actions undertaken by a highly unpredictable autocrat, US President Donald Trump, who once again occupies the White House.Not only do he and his appointees not believe in anthropogenic climate change, but they are intentionally attempting to ignore, reduce and destroy American science on many fronts.Since January 2025, scientific and medical programs and employment in the US federal government and in the universities have been drastically curtailed, with serious implications for that country and others around the globe.The cutbacks have been enormous and unnecessary (i.e., they are ignorant ideological decisions) and are likely to push American science and society back many years.As a concerned scientist, I have followed the development of this situation closely, especially through coverage in the AAAS journal Science.From January to August 2025, over 100 articles have described the situation 1 .Highlights of the changes and impacts are summarized here, with a commentary on how the NSIS and its membership should respond in this crisis.Given previous decadal US leadership and contributions in science, we are all affected and must respond.As Caplan (2025) and others (e.g.Thorp 2025) have said, "don't wait out four hard years, speak truth to power now, and come together and not be silenced".Above all, speak up for science!The suspensions and cuts to the US federal government employees and programmes to date have decimated the weather forecasting service and climate research programs of NOAA, health research in many federal departments charged with disease control, vaccine development and distribution, advanced medicine, and overseas aid for food and medicine.The department of education is now shut down.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.119 | 0.041 |
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".