Maple Leaf Zeitgeist? Assessing Canadian Prime Minister Justin Trudeau’s Policy Changes
Bibliographic record
Abstract
In the October 2015 elections, the charismatic Justin Trudeau led the Canadian Liberal Party to its first majority government in 15 years, overturning nearly a decade of conservative government. His premiership is generally considered to have begun well. This article examines Trudeau’s conduct of the election campaign, his choice of a young and diverse Cabinet, his courtship of the media and image making, and assesses changes in foreign and domestic policy. These have yet to prove substantive but Trudeau has signalled a reversal of Stephen Harper’s conservative policies and especially in regard to migration has tapped into images of ‘compassionate Canadians’. In foreign policy, this has been evidenced in relations with the United States and with a re-engagement with the Commonwealth especially in its soft power aspects. Trudeau’s green credentials and stance on Climate Change are a contrast to those of his predecessor but he has yet to confront the different environmental profiles and policies of the Canadian states. Canada’s Strategic Partnership with the European Community and the ratification of CETA are priorities and he has to come to terms with the implications of Brexit.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.033 | 0.011 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".