Bibliographic record
Abstract
Situating the CaseIn this chapter, I review Canadian electoral history to show how the system does -and does not -differ from its appropriate comparators.The chapter also serves as an agenda for the rest of the book.I start by introducing the parties individually and typologically.The typology is not airtight, but it underscores that different parties have distinctive dynamics.The account is not blow by blow, nor does it plumb the depths.The point, rather, is to provide just enough narrative to motivate cross-national comparison.I begin that comparison by moving up to the highest level of aggregation, with index numbers for fragmentation, volatility, and federalprovincial discontinuity in whole electorates.I show that fragmentation of the system does not correspond to predictions from the neo-Duvergerian model of electoral coordination.Indeed, it flat out controverts them.The Canadian system is more volatile than the others but not in a chronic sense.Rather, the volatility is episodic, startling punctuations of what is usually a stable system.Discontinuity between federal and provincial arenas is stunning.I then move down the aggregation ladder to differences among groups, as captured by survey evidence from individual voters.Although the patterns here reflect observations already on the record, I bring the evidence up to date and present it in a way that enables cross-national comparison.The setup also highlights how puzzling these patterns were and are.I conclude the empirical section by returning to the individual party as the analytical unit.The point is simple: all renderings of Canadian party
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.029 | 0.036 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 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".