Accounting for the Electoral Success of the Liberal Party in Canada Presidential address to the Canadian Political Science Association London, Ontario June 3, 2005
Bibliographic record
Abstract
Abstract.I show that the strong electoral success of the Liberal party in Canada stems in great part from the strong support of Catholics and Canadians of non-European origin. In the absence of the fervent backing it enjoys among these two groups, the Liberals would not be among the most successful democratic parties in the world. Yet, we do not have a good understanding of why Catholics and non-Europeans vote Liberal. I argue that the group bases of Liberal support should lead us to question the common interpretation that the party's centrist policy position is the key to its electoral success. Résumé.Je montre que le succès électoral du Parti libéral fédéral au Canada découle en bonne partie de l'appui des catholiques et des citoyens d'origine non européenne. Sans l'appui solide de ces deux groupes, le Parti libéral n'aurait pas remporté les succès électoraux remarquables qu'il a connus. Pourtant, nous n'avons toujours pas d'explication satisfaisante des raisons qui amènent les catholiques et les citoyens d'origine non européenne à voter pour le Parti libéral. Je soutiens que ces tendances sociologiques lourdes devraient nous inciter à remettre en question l'interprétation habituelle selon laquelle les succès libéraux sont attribuables aux positions centristes du parti.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".