Mohammad Azharuddin as a Theorist of Shock: The Life of an Indian Muslim Cricket Captain in the Time of Hindu Nationalism
Bibliographic record
Abstract
Abstract Mohammad Azharuddin's arrival in professional cricket served, to quote Karl Marx, as a reform of consciousness that awakened the sport ‘from its dream about itself’. His expertise with the bat invoked the wide expanse of human sensorium, provoking reactions of shock and admiration among observers. In this chapter, I examine Azharuddin's life in cricket and public through a dialectical probing of the relationship between shock and aesthetics. Azhar and cricket appear as a productive terrain to carry out the analysis, as it pushes the possibility of what or who can be considered as a valid subject for theoretical scrutiny. Taking cues from Walter Benjamin and CLR James, I theorise the shock effects created by a cricketer most unusual. From his wristy wizardry with the bat to his appointment as captain of the Indian men's cricket team during the rise of Hindu nationalism in the country, Azharuddin's presence and popularity extended beyond the boundaries that are often imposed on a sportsperson. Through his involvement in the match-fixing scandal that was exposed at the turn of the 21st century, Azhar (the name by which he was popularly known) challenged the mores of a game that had emphasised Victorian notions of purity on and off the field. For the purposes of this chapter, I discuss how Azhar constructed a bodily discourse that pushes us to reassess our very notions of art and aesthetics.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.033 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 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".