Caste, Class and Vote: Consolidation of the Privileged and Dispersal of Underprivileged
Bibliographic record
Abstract
This article attempts to examine the combined impact of caste and class on voting choices. By using data from National Election Studies conducted by Lokniti from 1996 to 2019, the article seeks to situate the findings in the larger temporal frame of a quarter of a century. This allows us to also examine if changing patterns of party competition affect the impact of caste–class combined. The article argues that two patterns emerge: one is the consolidation of the more privileged social sections in terms of class and caste and the other is the dispersal of the less privileged. The latter, by virtue of their political dispersal, are unable to shape as a political force in both electoral politics and in agenda setting. This finding is partly an extension of the earlier findings that politics of backward castes hit a dead-end and politics of the poor never emerged as an all-India political alternative. Together with the earlier experience, the findings in this article throw light on the limits of democratization and on the prospects of politics of the less privileged sections across the country.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".