Bibliographic record
Abstract
It is the world turned upside down. The Trump administration is warming up to historic enemies, starting with Russia, while taking a hard line with allies such as Mexico, Canada, Ukraine, and the EU. Europeans, in response, are questioning the survival of the transatlantic alliance, the backbone of the post–Second World War era. Although the divide centres on security, conflicts over tech and trade are mounting. The EU is racing ahead with strict rules designed to break up US tech ‘gatekeepers’, regulate social networks and ensure the safety of artificial intelligence. The US is sprinting in the opposite direction, promoting unbridled free speech, aggressive deregulation and destructive tariffs. China could end up the big winner. As transatlantic ties frazzle, Europe could close the door to US tech and become dependent on Beijing. China’s recent launch of a powerful, low-cost artificial intelligence model makes it a potential partner. The US, in turn, is jeopardising its biggest export market. Both Brussels and Washington must move back from the precipice. For Europe, the path forward means embracing tech, including US tech, not fighting it. For the US, it means allowing Europeans to regulate without threatening disastrous retaliation. And for those in Silicon Valley, the best strategy is to stay neutral and work as peacemakers, avoiding the temptation to side with Washington against Europe.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 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".