The New and Changing Transatlanticism: Lessons Learned and Future Challenges
Bibliographic record
Abstract
The transatlantic relationship is unusual for its cordiality (even if, at times, this characteristic is tested), intricate web of relationships, and resilience. There are innumerable examples of comity between bordering countries, such as the extraordinarily close relationship between Canada and the US despite sharing such a long border. Yet unlike the Canadian-American relationship – one that can be attributed to sharing the world’s largest repository of fresh water, breathing the same air, hundreds of citizens crossing their borders on a daily basis for work and leisure, speaking the same language, and sharing the bond of being the two former large British colonies of the Americas – the EU and the US have no such shared interests tied to propinquity and a colonial past. So it is all the more unusual, and thus, remarkable, that two entities – one a federal state, the other a quasi-federal sui generis construction – would expend so much time, energy, and human resources in building and maintaining the transatlantic partnership. This book has explored many factors that, together, help to explain the enduring transatlantic partnership – security, economic growth, and a shared vision of how to live and govern, in a globalized world. What conclusions can we draw about transatlantic policy processes and policy outcomes? In this chapter we return to the questions posed in Chapter 1 to draw conclusions about the “new” and “changing” transatlanticism: 1. How can we characterize the policy outcomes associated with the new transatlanticism? Which variables best explain policy outcomes? Are policies converging or diverging?
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.015 | 0.033 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".