Jagmeet Singh '05, Canada's Newest Political Star, Lifts His Party's Hopes
Bibliographic record
Abstract
OTTAWA — While Canada has long promoted multiculturalism, it took until this week for a major Canadian political party to choose a leader — Jagmeet Singh — who was not a white man or woman.\nBut Mr. Singh’s decisive win in the race to be the leader of the New Democratic Party, the furthest to the left of Canada’s mainstream parties, is far more than a symbolic victory for minority groups in the country.\nMr. Singh, 38, whose parents immigrated from India, shares many of the personal traits that made Prime Minister Justin Trudeau such an appealing candidate and helped propel him to power in 2015. The New Democrats, who so far have only ever achieved power at the provincial level, are hoping that Mr. Singh’s personal charisma, ethnic background and history as a social advocate may finally provide a formula that can lift the party to power at the federal level.\n“Canadians deserve a government that understands the struggles that people are facing right now,” Mr. Singh said on Sunday afternoon in Toronto, after the announcement of his victory. “It takes an act of love to realize we are all in this together, and an act of courage to demand better, to dream bigger and to fight for a more inclusive and just world.” Mr. Singh’s election underscores the already prominent role that Sikhs, who make up about 2 percent of Canada’s population, play in Canadian politics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.003 |
| Scholarly communication | 0.009 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.058 | 0.008 |
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".