Understanding Election Violence in the Philippines
Bibliographic record
Abstract
Despite election violence being a commonly agreed upon phenomena in the Philippines, there has been a dearth in academic research on the topic in recent years, largely due to a lack of reliable information. To address this, our article adapts recognized methods from studies such as Lindsay Shorr Newman’s 2013 paper, together with Stephen McGrath and Paul Gill’s 2014 research on terrorism and elections. To expose the timing of election violence, we tracked incidents relative to election dates for the period from 2004 to 2017, with the results indicating that violence increased closer to an election date, and frequency substantially increased during the 14-year period. This is the first academic journal article since John Linantud in 1998 to focus on the issue of election violence in the Philippines but through adaptive methodologies goes further, enabling national analysis. Furthermore, our findings reveal statistically significant differences regarding the types of terrorist attacks and targets when comparing election and non-election periods. We highlight complicating factors such as the majority of attacks being attributed to “unknown” actors and the complex situation during elections. The results also demonstrate that election violence in the Philippines is dominated by the New People’s Army and the use of assassination. The paper makes the case for further research and the creation of a dedicated database of election violence in the Philippines and elsewhere, and evaluates the measures implemented by the government that have failed to stem election violence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".