Triple Duel: The Impact of Coalition Fragmentation and Three-Corner Fights on the 2018 Malaysian Election
Bibliographic record
Abstract
Malaysia's previously hegemonic Barisan Nasional (BN) government was unexpectedly defeated in the 2018 general election despite a fragmented opposition and widespread three-corner fights that theory states should inhibit turnover. Why? We argue that the opposition-split hypothesis rests on three core assumptions: third parties split only the anti-incumbent vote; coalition/party support is relatively uniform across the country; and opposition parties are not “elite splits” in disguise. The Malaysian context challenges all three of these assumptions. Counterfactual election simulations ultimately suggest that the opposition split neither dramatically helped nor hurt the BN. While this does not upend conventional wisdom on opposition coordination, it does demonstrate that the theory manifests only when its assumptions accord with local realities. More substantively, our analysis also provides insights into why the new opposition will likely seek to increase the salience of ethno-religious issues in a bid to recapture electoral ground.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".