Confronting Epistemic Violence: The Kaurs' Resistance Against 1984
Bibliographic record
Abstract
This research investigates the epistemic erasure of Kaur’s (Sikh women) experiences of the Delhi Ghallughara in 1984, a state-enabled massacre of Sikhs in postcolonial India. Employing abductive analysis, three datasets were analysed: documentaries providing oral accounts of survivors and witnesses, archives of newspaper excerpts, and legal reports. Adopting the lens of Southern decolonial feminist framework rooted in Sikh epistemologies, Qualitative Content Analysis (QCA) and Critical Discourse Analysis (CDA) were implemented to explore: (1) the communal and gendered dimensions of 1984; (2) state-enabled structural silencing (3) how narratives have been re-narrated, through memory and epistemic erasure. Findings reveal that the Kaurs were not simply passive victims of communal violence, due to their gendered vulnerabilities, but they navigated immense violence with resistance and strategic silence. However, their testimonies remain underrepresented in newspapers, legal reports, and scholarly work. This paper argues that such erasure is not incidental, but rather systemically reinforced by cultural and patriarchal norms that intersect with political and institutional hierarchies. By centring the voices of Kaurs of 1984 and representing their stories, this study challenges dominant modes of knowledge production and calls for feminist reparative justice by legitimising their epistemologies and challenging systems that continue to silence them.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".