Perspective Gerrymandering and its Effects in Singapore’s 2025 General Elections
Bibliographic record
Abstract
Singapore experienced extensive electoral redistricting six weeks before polling day on May 3, 2025. Were the electoral boundary changes "gerrymandering in plain sight" as claimed by an opposition leader? And if so, did the People's Action Party (PAP) benefit from the redistricting and malapportionment to maintain its legislative supermajority? This critical perspective finds that gerrymandering methods such as "packing," "cracking," and "stacking" were used in the redistricting of many new and altered constituencies. Based on the limited geospatial and electoral data available, the findings also show that the PAP gained in vote shares in all the altered and newly created group and single-member constituencies, as compared to the opposition parties, since the last general elections. While malapportionment and electoral disproportionality have both improved, creating more single-member seats and removing the larger five-member group constituencies would achieve fairer apportionment and more equal representation of voters. Electoral reform would remove any concerns that the PAP won based on an unfair advantage.
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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".