Bibliographic record
Abstract
In the academic field, debates in the discipline of history largely contest whether the people whose narratives are absent in the dominant archives of knowledge, including Dalits, can be considered as devoid of history. Such contestations raise queries about the ways in which these groups form a sense of their past. In this light, can we consider cultural forms of narrative as reliable and ‘valid’ means to form an understanding of past, and to what extent? Can the cultural narrative forms, particularly autobiographical accounts, be utilized to reflect on the past of these communities? What methodologies does such an approach demand, and what challenges does it pose? This paper shall grapple with these intriguing inquiries. It attempts to position Dalit autobiographies and their utility in locating the sense of their past and in the larger knowledge production. This paper fundamentally proposes that Dalit autobiographies can lend crucial insights into the history of Dalit communities and beyond. These autobiographies can provide a perspective ‘from below’ and contribute to understanding how Dalits made sense of their past into narratives. I argue that Dalit articulation of their life experiences in the form of autobiographies not only rupture the assumptions of a singular past but also foregrounds the multiplicities and specificities to their everyday experiences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".