Assembly Size and Electoral Distortion in an SMP System
Bibliographic record
Abstract
There is a vast literature in political science concerning the strengths and weaknesses of single member plurality (SMP) electoral systems. Some argue that PR systems are superior because they ensure better representativeness by reducing the distortion between votes received by a party and its seat share. Others say that the benefits of SMP in terms of accountability make the price of electoral distortion bearable. But what if there would be incremental institutional changes that could maintain the benefits derived from SMP elections and still reduce the distortion it causes? In this paper, we make use of an innovative research design to measure the impact of assembly size on seat disproportionality as measured by the Gallagher Index. We make use of Canada as an ideal case. In this country, federal and provincial elections occur at regular intervals, and the numbers of seats at play vary substantially between levels of government within a province. We find that increasing assembly size is associated with reduced disproportionality in a negative logarithmic fashion, making it an especially useful institutional tool to reduce distortion in smaller assemblies. We argue this research brings a new light on an ongoing debate about SMP systems.
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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".