Dislocation, Displacement and Immigrant experience in the Short Stories of Shauna Singh Baldwin
Bibliographic record
Abstract
The Indian Diaspora is a wonderful place to write from, and I am lucky to be a part of it-Kiran Desai
 Indian Women writers like Kiran Desai, BhartiMukherjeee, Chitra Banerjee, Jumpa Lahiri all are dealing with the issues of Diasporic Consciousness, dislocation, displacement and immigrant experiences in their writings. Shauna Singh Baldwin, a Canadian-American writer of Indian origin is one of the most significant writers of Indian diaspora writing experiences of Sikh community during partition of Indian and its aftermath. In molding the personality of Shauna Singh Baldwin, the concept of nation, home and belongingness to the place of origin finds an important role. She has adopted and assimilated the elements of both home and host cultures and that is clearly revealed through her writings. As she says: “I wrote because I needed to make sense of my world by describing it. Eventually the stories weren't about me and my experience, but about situations, problems, feelings, metaphors and images that just refuse to go away.”
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".