Bibliographic record
Abstract
This article demonstrates how the formation of new federations with power asymmetries, such as Canada at the time of Confederation—its constitutional moment—led to asymmetric representation, also referred to as malapportionment. The same power asymmetries that led to asymmetric representation also led to fiscal redistribution in which some sub-national jurisdictions obtained a share of federal government spending that was disproportionate to their share of the country’s population or their contribution toward federal government revenues. This article makes use of historical institutionalism to demonstrate that Confederation can best be viewed as a critical juncture and treats Canada as a case study in which both political malapportionment and fiscal redistribution were part of the initial federal bargain. It also shows how path dependence ensures that both malapportionment and fiscal redistribution have been persistent features of the Canadian federation and have become accentuated over time. Malapportionment is measured using the Loosemore-Hanby index of electoral disproportionality, as adapted by David Samuels and Richard Snyder. This permits comparisons between institutions, including the House of Commons and Senate, various caucuses within those institutions, and the federal cabinet. It also permits comparisons between jurisdictions and between different time periods. Fiscal redistribution is measured using Statistics Canada estimates of spending by the federal government in each province and territory and the amount of revenue it collects in those same jurisdictions. Progressive forms of taxation, direct spending by the federal government, and intergovernmental grants have all expanded exponentially and have led to increased fiscal redistribution.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".