Bibliographic record
Abstract
The Liberal Party of Canada has governed for 78 of the last 110 years, making it the most successful political party in the world. How has one party been able to dominate the polls during such a tumultuous sweep of history? Will it continue to win? In The Big Red Machine , astute Liberal observer Stephen Clarkson tells the story of the Liberal Party’s performance in the last nine elections, providing essential historical context for each and offering incisive, behind-the-scenes detail about how the party has planned, changed, and executed its successful electoral strategies. Arguing that the Liberal Party has opportunistically straddled the political centre since Sir John A. Macdonald – leaning left or moving right and as circumstances required – Clarkson also shows that the party’s grip on power is becoming increasingly uncertain, having lost its appeal not just in the West, but now in Québec. Its campaigns now reflect the splintering of the party system and the integration of Canada into the global economy. An ideal political primer, deftly written and filled with a wealth of fact and analysis, The Big Red Machine is a fascinating history of Liberal pragmatism, communication tactics, and dramatic changes in leadership style. "Even if the last century did not belong to Canada, Canada turns out to have belonged to the Liberal Party," Clarkson concludes. Although he foresees considerably less rosy prospects for Grits in the years ahead, the "big red machine" remains a formidable political force.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.050 | 0.010 |
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".