Bibliographic record
Abstract
US President Donald Trump signed Executive Order 13769 (Protecting the Nation from Foreign Terrorist Entry into the United States). 1 Commonly known as the Travel Ban or the Muslim Ban, the Order suspended the US Refugee Admissions Program for 120 days, placed an indefinite ban on refugees from Syria, and barred entry to anyone from seven predominantly Muslim countries in the Middle East and Africa.The next day, Canadian Prime Minister Justin Trudeau went on Twitter to proclaim: "To those fleeing persecution, terror & war, Canadians will welcome you, regardless of your faith.Diversity is our strength #WelcomeToCanada." 2 Fourteen minutes later, Trudeau tweeted a 2015 picture of himself greeting a Syrian refugee child at the airport.These two Tweets, a declaration of hospitality and visual evidence of this hospitality, exemplify Canadian "humanitarian exceptionalism," a belief that what sets Canada apart from the US and other nation-states is its distinct benevolence and commitment to human rights.The welltimed public pronouncement was a strategic and politically expedient response to the devolving political situation in the US, which has gone on to implement Immigration and Customs Enforcement raids, separate border-crossing parents from their children, and indefinitely detain asylum seekers in concentration camps.Trudeau draws on a tradition that defines Canadian liberal nationalism -qualities of generosity, hospitality, and tolerance -against our southern neighbour's restrictive and ruthless actions.The president's widely condemned racist and xenophobic Order provided the prime minister with an opportunity to exalt Canada as an open and welcoming haven to the global public.At a moment of American humanitarian failure, Canada asserted itself as a leader in refugee humanitarianism.The subsequent trending of the hashtag #WelcomeToCanada on Twitter attests to the ways that the
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.058 | 0.017 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.039 | 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".