The Dialectic of Dialectical Materialism and Discourse: A Scrutiny of Aravind Adiga’s The White Tiger
Bibliographic record
Abstract
The powerful social class exercises its hegemonic practices mainly on the basis of cognitive and discursive strategies. These strategies are accomplished through the exploitation of social knowledge, identities and ideologies, which, for their constitution, owe to the cognitive and discursive tactics themselves. The hegemony of the powerful social groups may, however, be countered when the manipulated individuals come to achieve enough knowledge and realization which protect their cognition from being manipulated further. Moreover, this achieved knowledge and realization also enables them to adapt themselves to the cognitive and discursive practices of the dominant class for the improvement of their socioeconomic position. The paper scrutinizes this notion in Aravind Adiga’s The White Tiger. The study applies Marx’s Dialectical Materialism and van Dijk’s concept of Discourse and Manipulation. Additionally, the latter perspective also works as a model for the research. The study elucidates that the poor-rich divide, which is prevalent in the Indian society, is mainly created by the dominant class through their manipulation of the cognition of the dominated class. It also highlights the productive attempt by a lowest of the low caste individual to become a successful entrepreneur by adapting to and using the same cognitive and discursive tactics as employed against people like him by the powerful social class.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.087 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| 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".