Appropriation in Guise of Tolerance: Neo-Hinduism and its Reception of the 'Other'
Bibliographic record
Abstract
Hinduism, in the writings of Indian (Hindu) intelligentsia in the nineteenth century and beyond, right from Radhakrishnan to Amartya Sen, has been eulogistically portrayed as very tolerant and receptive of other religions-cultures. No knowledge is ever neutral; rather it serves the purpose of those who produce it. This article, therefore, re-examines these (often hyperbolic) claims and the underlying motivations involved therein. This is not however to say Hinduism is/was intolerant. But the objective of the paper is to scrutinize the politics of the truth claim in saying that Hinduism is tolerant and the nature of identity politics inherent therein. The article demonstrates ‘how one [read: the ‘modern-secular’ Indian] construes oneself in the present expresses the continuity between how one construes oneself as one was in the past and how one construes oneself as one aspires to be in the future’. (Weinreich & Saunderson, 2004: 120) It points to how in the collective memory (of the ‘secular’ Indian) Hinduism has always been construed, needless to say anachronistically, in tandem with the idea of 'India' which is barely a few decades old. The article reveals how this politics of remembering oneself within the discursive legacy of purported ‘tolerance’ actually dismembers certain ethnic groups from one’s cultural past.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.067 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".