Writing Imagined Diasporas: South Asian Women Reshaping North American Identity
Bibliographic record
Abstract
Joel Kuortti's Writing Imagined Diasporas: South Asian Women Reshaping North American Identity is a study of diasporic South Asian women writers. It argues that the diasporic South Asians are not merely assimilating to their host cultures but they are also actively reshaping them through their own, new voices bringing new definitions of identity. As diaspora does not emerge as a mere sociological fact but it becomes what it is because it is said to be what it is, the writings of imagined diasporas challenge national discourses. Diaspora brings to mind various contested ideas and images. It can be a positive site for the affirmation of an identity, or, conversely, a negative site of fears of losing that identity. Diaspora signals an engagement with a matrix of diversity: of cultures, languages, histories, people, places, times. What distinguishes diaspora from some other types of travel is its centripetal dimension. It does not only mean that people are dispersed in different places but that they congregate in other places, forming new communities. In such gatherings, new allegiances are forged that supplant earlier commitments. New imagined communities arise that not simply substitute old ones but form a hybrid space in-between various identifications. This book looks into the ways in which diasporic Indian literature handles these issues. In the context of diaspora there is an imaginative construction of collective identity in the making, That a given diaspora comes to be seen as a community is the result of a process of imagining, at the same time creating new marginalities, hybridities and dependencies, resulting in multiple marginalizations, hyphenizations and demands for allegiance. The study concentrates on eleven contemporary women writers from the United States and Canada who write on South Asian diasporic experiences. The writers are Ramabai Espinet, Jhumpa Lahiri, Amulya Malladi, Sujata Massey, Bharati Mukherjee, Uma Parameswaran, Kirin Narayan, Anita Rau Badami, Robbie Clipper Sethi, Shauna Singh Baldwin, and Vineeta Vijayaraghavan.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.016 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".