Qualitative Analysis of Maoist Strategy and Rebel Agency
Bibliographic record
Abstract
While chapter 3 outlines how colonial indirect rule created the structural conditions for Maoist insurgency through multiple causal pathways, it also emphasizes that rebel agency in the form of ideological frames and organizational legacies plays an equally important role in mobilizing the ethnic networks of lower castes and tribes. In this chapter, I provide qualitative evidence to demonstrate this interaction between rebel agency and structural conditions created by colonial institutions. I first provide a detailed history of the organizational evolution of the various Maoist factions since the 1960s. Then, I use fieldwork interviews and textual analysis of Maoist documents to demonstrate that the Maoists are strategic about their choice of area, based on local politics, terrain, ethnic composition, and level of economic development. However, even within those areas they considered favorable based on terrain, inequality, and politics, they were successful only where the British colonial institutions of zamindari (landlord) land tenure or certain types of princely states were present. This demonstrates that rebel agency interacts with and is constrained by the opportunity structures of land/ethnic inequality available in areas of former indirect rule and revenue collection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".