Heterogeneous Electoral Constituencies Against Legislative Gridlock
Bibliographic record
Abstract
Abstract Legislative gridlocks, driven by social partisan sorting, pose a significant threat to contemporary democracies. In this paper, I argue that this problem can be addressed by replacing geographic electoral constituencies, which group voters by area of residence, with heterogeneous electoral constituencies, which are based on random assignment and thus reflect the diversity of the entire electorate. I show that geographic electoral constituencies are likely to crystallise cleavages that reinforce geographic divisions, whereas heterogeneous electoral constituencies are likely to dilute deep social divisions. I argue that heterogeneous constituencies have this effect not because they suppress intergroup difference, as is commonly held, but rather because they encourage political parties to express cross-cutting social identities. The politicisation of cross-cutting social cleavages prevents social partisan sorting and moderates political conflict. Heterogeneous electoral constituencies should therefore be considered as part of an expressive institutional response to the democratic threat of legislative gridlock.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".