Violence and Impunity: Democratic Backsliding in the Philippines and the 2022 Elections
Bibliographic record
Abstract
As president from 2016 to 2022, Rodrigo Duterte captured the judiciary, dominated the legislature, attacked the media, and presided over a campaign of mass killing, leaving an estimated 30,000 alleged drug criminals dead. Despite wielding vast amounts of power, Duterte stepped down after the national elections on May 9, 2022 in a largely peaceful transfer of power to Ferdinand Marcos Jr., son and namesake of the former dictator deposed in 1986. Why did Duterte amass power without causing full democratic collapse into authoritarian rule? The Philippines experienced backsliding to competitive authoritarianism: while elections remain free and somewhat fair, other features of democracy like civil liberties and political freedoms have eroded badly because of mass violence. The Philippine case demonstrates the autocratizing e ect of an emerging form of political violence: a focused campaign of state terror that produces fear and electoral success. I present evidence from two cases—the national "war on drugs" and its local antecedent in Davao City—to explain how violence escalates, provokes accountability, evades culpability, and contributes to democratic backsliding without immediate collapse to authoritarianism. With the election of Marcos Jr., the impunity of the former incumbent is likely to become institutionalized, and democratic backsliding is unlikely to be reversed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".