Historicizing Brati: A Journey of Sujata’s Memories in Mahasweta Devi’s Mother of 1084
Bibliographic record
Abstract
Mahasweta Devi’s Mother of 1084 has a trope of returning to the past events, after a tragedy. This trope is central to both the plot and act of narration of the protagonist, Sujata. Sujata’s journey to the past is not just part of a thematic element, but also a narrative strategy through which Mahasweta Devi casts an indirect gaze on the Naxalite world. This study analyses Sujata’s trip into the past by focusing on different subsets of memory involved in her journey which Mahasweta Devi employs throughout the novel. Devi’s Mother of 1084 entirely is a narrative from the perspective of a bereaved Sujata after having lost her son Brati to unknown circumstances. By referring to the theoretical underpinnings of renowned scholars from the field of Memory Studies, including Jacques Lacan, Endel Tulving, Chris Brewin, and Kurt Danziger, this study explores how different memory subsets that of recollection, reminiscence, episodes, semantics, and flashbacks of the characters in the novel come together to historicise the memory of the deceased (Brati). The study then attempts to understand memory’s role as the focal point in bringing an artistic sense to the novel.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".