Bibliographic record
Abstract
The Truth About Trudeau nick romanowLike many other immigrants, I speak differently than most.This generally prompts the question, "Where are you from?"My answer: "Canada." In the era of Trump, this answer often invites exclamations about how wonderful it must be to have Prime Minister Justin Trudeau as my nation's leader.Canadians must be so grateful to have such a charismatic symbol of a progressive, open society.Well, not all Canadians.If you were to poll students on a left-leaning American college campus about their opinions regarding Justin Trudeau, you would likely see overwhelming support.The Canadian population would largely disagree: Trudeau's approval currently sits at 47% according to the Canadian Broadcasting Corporation, CBC, with numbers as low as 35% in the past.For some perspective, the widely-disliked Donald Trump has a similar alltime low.My most recent visits home reveal this discontent.My family and I are from the rural, western province of Saskatchewan.Grossly generalizing, Saskatchewan would be the Canadian equivalent of Nebraska.Saskatchewan is unique for being geographically flat, economically agrarian, and, most notably, politically conservative.All of the "Prairie" provinces -Alberta, Saskatchewan, and Manitoba -share in their political leanings, but Saskatchewan is perhaps a prime example of what it means to be conservative in Canada.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".