Bibliographic record
Abstract
Redistributing electoral ridings alters their number, revises their boundaries, or does both at the same time. Ostensibly, the purpose of redistribution is to adjust parliamentary representation for population changes - the growth or decline of population, or shifts in its territorial distribution and social composition. Before an arm's-length commission, headed by a judge, took control of electoral redistribution in the 1960s, parliament - effectively, the majority party - controlled redistribution, raising the possibility that the governing party would adjust the ridings for its own advantage, a practice known as gerrymandering. Providing detailed analyses of parliamentary redistribution in Ontario that preceded the province’s commissioned ridings of the 1960s, George Emery's Principles and Gerrymanders unravels the mechanisms, operational strategies, and exposure to partisanship of parliamentary redistribution and its influence on general election outcomes. Using quantitative research methods, Emery identifies gerrymanders and demonstrates empirically whether or not these worked. He closes with a discussion of the transition to commissioned ridings, what has changed in redistribution, and what continues from the era when parliament redrew ridings. Contextualized with detailed maps and political cartoons, Principles and Gerrymanders is a pioneering study and a major contribution to the literature on Canadian and Ontario political history.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.041 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".