It Takes a Village to Raise a Rebel: Prewar Social Orders and Insurgent Mobilization with Evidence from the Philippines
Bibliographic record
Abstract
When armed groups strike the balance between coercion and consent in their interactions with civilians, how can civilians assert their interests within this insurgent-civilian dynamic? This thesis argues that variations in prewar civilian social arrangements and political orders can influence the outcomes of insurgent mobilization by providing civilians with leverage over insurgent organizations. When insurgents seek to embed within a civilian community, they inevitably engage with pre-existing institutions and social norms to manage the insurgent-civilian relationship. This process can empower civilians to affect the scope and duration of rebel activities. I argue that when prewar social norms and political orders are more hierarchically arranged, this lowers the transaction costs of insurgent mobilization. By forming strategic alliances with local elites who control concentrated power and resources, insurgent organizations can achieve rapid growth in organizational capacity. However, these alliances also make insurgents vulnerable to elite capture, wherein powerbrokers use their leverage over armed groups to pursue private interests, potentially prolonging mobilization. Conversely, when prewar social norms and political orders are more egalitarian, insurgents face higher transaction costs of mobilization due to the time and effort required to access and accumulate power and resources dispersed within the civilian community. This diffusion can dampen insurgent capacity, potentially limiting the scope and duration of their mobilization. Using a mixed-methods approach, my quantitative analysis demonstrates that the ordering of pre-war social and political arrangements affects conflict duration, mobilization duration, likelihood of conflict recurrence, and the scope of insurgent objectives. Primary data from the Cordillera and Mindanao regions of the Philippines provide qualitative insights into the mechanisms by which these prewar social processes can influence the trajectory of insurgent mobilization. This research contributes to the social-institutionalist literature on insurgent mobilization by focusing on the civilian side of the insurgent-civilian relationship.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".