Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Biden’s administration took office when the US’s facing the global challenge of pandemic and the recession. Changes in American policy, particularly as concerning international trade, immigration; climate policy initiatives, represent new effects and challenges. Rejoining the Paris climate agreement was one of the first decisions of the new administration. The climate agenda means keeping global warming under control; and trade policy may be part of this process. Making economic development more inclusive and decarbonized, introducing new fuel efficiency standards needs time; as CO2 emissions per capita in US and Canada now are much higher than in France, Great Britain, Germany. Although president Biden entered office having majority in both chambers of Congress, it may not be enough for passing legislation concerning some aspects of his policy agenda, including climate, budget, immigration. So the President started to realize his policy immediately after inauguration through executive orders. Addressing the trade issues, environment agenda, the new administration underlines that approach to international economic relations will be different (from Trump’s priorities), restoring American leadership abroad. Being at the pathway toward recovery at the beginning of the current year, the American economy still needs support and stimulus under struggling with pandemic consequences.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it