Bibliographic record
Abstract
Anyone with a passing understanding of Canadian politics is aware of the stubborn presence of party discipline in the parliamentary system. It is not a phenomenon that has been left to the stuffy corners of the ivory tower. Political actors and the media have complained about party discipline for decades. Reforms have been proposed; party leaders have promised new ways forward. As a central trait of Canadian Parliament, party discipline has driven away voters—it has even inspired the development of new political parties. What role can Canadian political science play in understanding party discipline 75 years after these familiar sentiments appeared in the predecessor to this journal: “How could this control [party discipline] be destroyed, and the individual member be made an independent critic of government and of legislation, and a responsible servant of the people” (Morton, 1946: 136)? It turns out Canadian political science has much to offer. With the publication of J. F. Godbout'sLost on Division: Party Unity in the Canadian Parliamentand Alex Marland'sWhipped: Party Discipline in Canada, 2020 has been a monumental year for the study of Canadian Parliament and political parties.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.046 | 0.030 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".