Tracing Hijra Ethnicity in Indian Transgender Autobiographies: Revisiting the Erased Hijra Legacy through Trans Self-memory
Bibliographic record
Abstract
Transgender people in India are categorized under various regional and culturally bound terms. Hijras is one such transgender category indigenous to the religious and cultural history of the land. They are considered ethnic clans because of their self-identification with Hijra legacy. This article critically explicates Indian transgender autobiographies as narrative accounts of the collective experiences of transgender communities, transgressing the borders of self-memory to collective memory and consciousness. Transgenders experiencing trauma from victimization are bereft of agency and autonomy to assert their epistemic value in the discursive process. Heteronormative narrative discourses subvert transgender subjectivity, perpetuating normative modalities that result in epistemic amnesia regarding transgender concerns. Individual transgender autobiographical narratives become the assertion of epistemic agency rooted in trans subjectivity, representing the collective legacy of the hijra clan. Hijra autobiographies are the panacea for the collective amnesia of normative society that obliterates the hijra cultural legacy. The authorial narrative diegesis evidences the replication of customs and rituals of the hijra heritage in modern milieu.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".