“Love Jihad”, “Forced” Conversion Narratives, and Interfaith Marriage in the Sikh Diaspora
Bibliographic record
Abstract
This paper sets out to critically examine the “forced” conversion narrative circulating across the Sikh diaspora. The “forced” conversion narrative tells the story of Muslim men allegedly deceiving and tricking “vulnerable” Sikh females into Islam. The paper explores the parallels between the “forced” conversion narrative and the discourse on “love jihad” propagated by the ruling Bharatiya Janata Party (BJP), as well as drawing out its particularities within the Sikh community. The paper is informed by new empirical data generated by a series of qualitative interviews with Sikhs in the UK, US, and Canada, and captures the complexities and nuances of my respondents in their interpretations of, and challenges to, the “forced” conversions narrative. The paper adopts a decolonial Sikh studies theoretical framework to critically unpack the logics of the discourse. In doing so, it reveals a wider politics at play, centred upon the regulation of Sikh female bodies, fears of the preservation of community, and wider anxieties around interfaith marriage. These aspects come together to display Sikh Islamophobia, whereby the figure of the “predatory” Muslim male is represented as an existential threat to Sikh being.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.025 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".