Bibliographic record
Abstract
Carens and others-have, each in their own ways, challenged the traditional liberal h e w o r k of individual rights and sought to extend this fiarnework to accommodate cuItural and disadvantaged rninorities.The theoreticai prescriptions that How fiom these works have implications for re-ordering majority-minority relations in pluralist societies.However, most discussions of ttiis sort have usuaiiy involved thinking through examples fiom advanced liberai democracies.And since most couniries in the wodd today are culturally diverse, there is a need to analyze and use other, mostly non-Western, examples to illuminate our understanding of what justice requires in regard to identity conflicts and comrnunity nghts.My dissertation uses the Indian example to probe morally compelling issues pertaining to liberal justifications of rights for disadvantaged groups.It explores the challenges that cultural difference and group disadvantage pose to the ideal of equal citizenship.More specifically, it draws on caste and religjous identities to problematize the notions of cultural recognition and resource redistribution based on disadvantages that groups experience.At a concrete level, the analysis focuses on (a) cultural recognition for religious minonties, and (kt) affirmative action for disadvantaged groups.While arguing a case for broadening the referent of equai treatrnent to include fair strategies of inclusion for groups that find themseIves under the burden of unequal circumstances, the thesis also addresses the reasonable Iimits of such group-based clairns in a liberal dernocracy of India's size and diversity.A study of the indian model, it is argued, poses fiesh challenges and solutions to the theory and practice of liberal democracy in both western and non-western contexts. AcknowledgemeatsFirst and foremost, 1 would like to thank Prof. Joseph Carens for his guidance and patience throughout both the dissertation process and my entire graduate career.His intensive intellectual advice and fnendly demeanour have significantly conibuted in making the exercise of writing this dissertation an extremely pleasant one.My dissertation took shape in, and grew out of the passionate and lively discussions in
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".