Continuity, History, and Identity: Why Bongbong Marcos Won the 2022 Philippine Presidential Election
Bibliographic record
Abstract
In May of 2022, Bongbong Marcos won a commanding 59 percent of the vote to become president of the Philippines. His victory was, on some level, shocking to scholars and analysts of Philippine politics. As a result, a plethora of di erent theories have been proposed, in an attempt to explain why Marcos won. In this paper, we use nationally representative survey data to explore which factors predict (and do not predict) voting intention for Marcos. We find that, a) support for former President Rodrigo Duterte, b) positive perceptions of the late President Ferdinand Marcos and martial law, and c) ethnic (linguistic) identity are strong predictors of voting for Bongbong Marcos. On the other hand, age, education, and income are not. Consequently, theories based on continuity, coalition, history, and identity provide the most leverage on the question of why Bongbong Marcos won the election.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".