Post-Truth Politics in India’s Right-Wing Ecosystem: An Extended Critical Commentary
Bibliographic record
Abstract
The right-wing movement in India received an impetus in 2014 with the Bharatiya Janata Party (BJP), capturing governmental power at the national level. Among the fundamental traits of the right-wing movement in India, as in America, is what is called post-truth. The latter is a condition where blatant lies (or half-truths) are deliberately produced and spread on a massive scale, for an ideological and political purpose. The post-truth condition has important intellectual and political implications. For example, given its commitment to claims that are without any objective basis, the right-wing movement sees society as divided into groups on the basis of subjective criteria (e.g., religion). Thus it denies the objective basis for seeing a society as class-society. It also concomitantly denies the state as class-state. A directly political implication of post-truthism is the accumulation of lies by means of the suppression of dissent. The right-wing movement, including its post-truthism, does not hang in the air, however. It has a solid political-economic foundation. This article critically discusses the post-truth character of India’s right-wing movement, and explains how it is that the overall character of India’s capitalist economy is behind this. The broader arguments of the article have wider applicability beyond India.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.044 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".