Bibliographic record
Abstract
After four years of not simply inaction but significant retrogression in U.S. climate change policy, the Biden administration has its work cut out. As a start, it needs to undo what Trump did. The Biden administration took a step in that direction on Day 1 by rejoining the Paris Agreement. But simply restoring the pre-Trump status quo ante is not enough. The United States also needs to push for more ambitious global action. In part, this will require strengthening parties’ nationally determined contributions (NDCs) under the Paris Agreement; but it will also require actions by what Sue Biniaz, the former State Department climate change lawyer, likes to call the Greater Metropolitan Paris Agreement—that is, the array of other international actors that help advance the Paris Agreement's goals, including global institutions such as the International Maritime Organization (IMO), the Montreal Protocol, and the World Bank, as well as regional organizations and non-state actors. Although the Biden administration can pursue some of these international initiatives directly through executive action, new regulatory initiatives will face an uncertain fate in the Supreme Court. So how much the Biden Administration is able to achieve will likely depend significantly on how much a nearly evenly-divided Congress is willing to support.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".