Bibliographic record
Abstract
Abstract This article explains tactical escalation by a Peruvian left-wing group during the 1980s and 1990s as an interaction effect between organizational ideology and the broader political and organizational environment. In 1980, Peru's Sendero Luminoso (Shining Path) organization ended a decade of political organizing and launched armed struggle against a new civilian government. Peru had been governed since 1968 by military officers, but popular pressure, including strong left-wing protests, had forced the military to cede control. In responding to democratization with revolution rather than electoral participation, Sendero broke with the rest of Peru's Marxist left. In 1983, Sendero again escalated its tactics, initiating a campaign of violent intimidation against Peru's legal left. By 1996, according to data assembled for this study, the group had selectively assassinated some 300 prominent Peruvian leftists. For theorists of revolutions and social movements, Sendero's tactical trajectory poses two important puzzles. First, many revolutionary theorists believe that transitions from authoritarianism to elections decrease armed insurgency. Why, then, did Peru's democratization provoke Sendero's escalation? Second, Sendero might well have been expected to cooperate with other left-wing groups, rather than to attack them so brutally. Why did Sendero choose an alternative path? The group's anti-left measures are all the more puzzling given the opposition they provoked among potential allies at home and abroad. This article explains Sendero's choices by drawing on political opportunity theory, theories of organizational competition, and the concept of declining protest cycles. Democratization can promote greater levels of strife if small but violence-prone groups fear marginalization in electoral politics. A dense left-wing social movement sector, moreover, can stimulate internecine competitive fighting if only some of the movement's members accept the legitimacy of national elections.
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 | 0.001 | 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 | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".