Bibliographic record
Abstract
In this paper, I read Sunanda Sikdar’s memoir Doyamoyeer Katha ( Doyamoyee’s Tale , 2008, Bengali) and study how an East Bengali immigrant woman presents her perceptions beyond the nationalistic history as well as the key motifs of Bengali refugee past. I examine the development of Doyamoyee’s heterogeneous identity and emphasize the importance of revisiting stereotypical and canonical artworks produced by the immigrant bhadralok, through juxtaposing these works with non-bhadralok refugee experiences. The mainstream writings have emotionally used the Partition memory and repeatedly tinted everyday realities of rural East Bengal with identical cliches, whereas Sikdar’s writings, as I argue, are based on the absence of typified emotive tropes. By understanding the category to which Doyamoyee belongs and studying her heretical perspectives, this paper demonstrates the difficulties of confining her identity into a fixed category of refugee-ness as belonging to the middle-class.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".