Wandering Dervish of <i>Laila Majnu</i>: Sufism and Socio-Political Mobilization in Kashmir
Bibliographic record
Abstract
Historically Sufism has been portrayed as an acceptable mode of Islam in Bollywood and political Islam as a threat to Indian secular ethos. This has been particularly true of Kashmir films. However, Bollywood’s emphasis on the syncretic dimension of Sufism is a misappropriation of Kashmir’s Islamic history. This study establishes that Kashmir’s Sufism has to be seen as a socio-political movement that has been instrumental in liberating the lower caste Kashmiris from the Brahminic hegemony. Also, Islamization in Kashmir has to be seen as a historical evolution of a community, rather than a transition from one set of beliefs to another. The syncretization of Hinduism and Islam in Hindi cinema is an assertion of the postcolonial nationalist imagination of a secular India. This assertion is a product of the anxieties of loss of the territory with which the nation shares a turbulent relationship. Sufism conceived of in this way becomes instrumental in ideological co-optation of the Muslim-majority Kashmir. The study probes Laila Majnu’s (2018) wandering dervish protagonist to chart out the representation of “individualistic” and “renunciatory” dimension of Sufism that couches Orientalist notions of the primitive and repressive “Muslim mind”. The apolitical narrative of the film mandates a critical inquiry since it is set amidst the politically charged atmosphere of Kashmir.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".