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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".