Reclaiming Voices: Autobiographical Journeys of Bama, Urmila Pawar, and Shantabai Kamble
Bibliographic record
Abstract
This research article aims to explore the autobiographical narratives of Bama, Urmila Pawar, and Shantabai Kamble, three renowned women writers whose works have challenged societal norms and given voice to marginalized communities. The focus of the study is to analyze their autobiographical works like, Karukku, The Weave of My Life: A Dalit Woman’s Memoir, and The Kaleidoscope Story of My Life as a powerful tools for resistance, redefinition, and recounting of silenced experiences of Dalit women. The article examines the ways in which Bama, Urmila Pawar, and Shantabai Kamble resist social and cultural constraints through their autobiographical writings and it explores how these women redefine their traditional narratives and challenges of dominant discourses through their life stories. This article investigates the role of recounting silenced experiences in fostering empowerment, social change, and cultural understanding of their life. The research analyzes the thematic similarities and differences in the autobiographies of the selected writers and their contributions to feminist and Dalit literature. The research article highlights the significance of reclaiming voices and the transformative potential of autobiographical narratives in addressing social injustices and promoting inclusivity. Despite the recognition of autobiographical writings as powerful tools for marginalized voices, there is a lack of comprehensive study. This article aims to fill this gap by closely examining their works, analyzing how their narratives challenge oppressive structures, and emphasizing the transformative potential of their stories. By doing so, the research aims to contribute to the broader understanding of feminist and Dalit literature and underscores the significance of centering marginalized voices in academic discourse.
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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 | 0.002 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| 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".