Bibliographic record
Abstract
For Donald Trump ‘America First’ means ‘America First.’ Canada and likeminded nations will have to get used to it. Canada will have to actively engage with Congress, the states and the private and public interests that drive the American agenda. We will also have to put more effort and contribute more to the rules-based order of which we have been a beneficiary. Traditional statecraft is based on predictability and stability, both hallmarks of U.S. post-war foreign policy practised by both Democrats and Republicans. Predictable, Mr. Trump is not. The deliberation and careful planning that characterized the Obama administration have been replaced by Mr. Trump’s reliance on gut and instinct. Such unpredictability will continue to create heartburn inside foreign chancelleries, whether friend or foe. Where once the USA was prepared to cover the spread on trade and security, under Donald Trump there will be more take than give. Now, Canada and the allies will have to make their own investments in hard power to preserve collective security. But less dependence and reliance on US leadership and more collective responsibility would be a good thing. Middle powers, like Canada, will have to step up their diplomacy, both collective and individual. Focusing on their own niche capacities they will have to shore up the space left by Trump Administration decisions on climate, migration and at the international institutions that sustain the rules-based order. Ironically, one effect of the Trump presidency may be to make the western alliance stronger.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.075 | 0.030 |
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".