Bibliographic record
Abstract
Abstract. This paper explores the targeting of illegal electoral strategies and identifies the voters most vulnerable to vote buying and coercion. The literature on social networks shows social ties and connectedness to have positive effects on a number of metrics related to politics, especially political participation. At the same time, politicians can take advantage of such network ties to engage in electoral strategies that subvert democratic processes. The same types of network structures that facilitate political participation and cooperation in established democracies may also make it easier for politicians in consolidating democracies to identify and monitor voters, facilitating illegal electoral strategies. Using a survey of 864 households conducted in Isabela Province, Philippines, I find that individuals with more social ties are disproportionately targeted for vote buying, while individuals who discuss politics with their network are targeted for electoral violence or intimidation. Understanding these mechanisms can help policymakers and local NGOs design more effective voter education initiatives and better address the needs of groups that are vulnerable to these practices. ∗Draft dissertation chapter, please do not cite without permission.
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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 | 0.002 | 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 | 0.001 |
| 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 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".