Bibliographic record
Abstract
In the most recent election for the House of Commons, the Liberal Party led by Justin Trudeau scored a resounding victory over the Conservative Party led by Stephen Harper. Harper had held leadership over Canada for nearly a decade. When the campaign started, the Conservatives, the New Democratic Party (NDP), and the Liberals seemed to be in a tight race. It was not an unrealistic scenario that Canada would have the first-ever NDP government. However, as time went by, the popular support for the NDP went on the wane with the Tories remaining in the last-stage battle with the Grits. In the home stretch, Trudeau successfully outdistanced Harper. The Liberals now occupy 184 seats in the lower house, which clearly goes over the majority line. The Greens kept the leader's seat on Vancouver Island. The balance theory and the bandwagon theory offer little to explain the Liberals' win in Ottawa. It was indeed the young, good-looking Trudeau that appealed to the Canadian voters to change the governing party but there are more to explain the outcome of the election. The Liberals were able to expand their range of support rightward and leftward to snatch the votes from the Conservatives and the NDP. The swaying pledges were mainly domestic policy, mixed subtly with foreign policy. Trudeau's Liberal Party was able to persuade nearly 40 percent of the sensible Canadian voters of an alternative way Canada should move forward from the predecessor.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.385 | 0.258 |
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".