Intra-party Competition and Local Democracy in the Indian States: A Statistical Analysis
Bibliographic record
Abstract
India provides an excellent laboratory within which to address the question of why and when governments choose to bring about democratic change at the local level. This is because, as described in Chapter 3, the decision of whether to establish or to maintain elected government at the village level has been largely left in the hands of Indian state governments. This feature of the Indian context makes it possible to examine the variation in the implementation of local democratization within the same national context across space and time. Explaining this variation provides a unique opportunity to understand the causes of local democracy while holding several confounding factors constant. There have been considerable differences across state governments in India in the degree to which they have established and strengthened democratic institutions at the local level. Some state governments have taken significant steps to establish robust democratic institutions at the local level and to devolve significant resources and powers to them. In these states, local democratic institutions or panchayats have played an important role in improving development and increasing citizen satisfaction (e.g. Heller, Harilal, and Chaudhuri 2007; Bardhan and Mookherjee 2006b). Other state governments in India have failed even in the basic task of holding regular elections to bodies at the local level, some even despite the compelling pressures of the 1993 national constitutional amendments described in Chapter 4. These state governments have chosen instead to let these bodies function for years without an electoral mandate or to suspend these bodies and have their functions taken over by unelected administrators. I argue that the differences in the behavior of state governments toward local democratic institutions can be explained in large part by the degree to which the state chief executive – the chief minister – faced competition within the party for control over its key organizational networks. Based on the discussion in Chapter 3, I contend that chief ministers in India tend to face greaterintra-party competition when at least one or more of the following conditions hold: (1) the party organization has existed for a relatively long period under the leadership of individuals other than the current chief minister and (2) the ruling party is divided with an experienced leader at the helm of the organizational wing.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".