Triple Duel: The Impact of Coalition Fragmentation and Three-Corner Fights on the 2018 Malaysian Election
Why this work is in the frame
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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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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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 it