Bibliographic record
Abstract
This article applies theories of diaspora and diasporic consciousness to the study of a film produced in Canada by an independent South Asian film maker, Srinivas Krishna. The film, Masala, a dark comedy filmed in Toronto against a backdrop of South Asian immigrant groups, multi-culturalism and the 1985 Air India bombing and drawing upon the magico-realism of Bollywood productions and Indian devotionals, offers commentaries on questions of identity and life among South Asian diasporic communities in Canada, Canadian social policy, religion and diasporic consciousness. The article examines the way the film is located in diasporic space and how the film maker plays upon the different locations and sensitivities of his audiences to represent, explain and critique diasporic space. The article considers the way caricature and stereotype inform the negotiations of identity among members of diasporic communities, the way objective and subjective spaces are redefined in a diasporic setting and how Hindu beliefs are represented in such settings. The article examines how the film maker draws upon traditional techniques of Indian story telling as well as a mixture of cinematic techniques to tell a diasporic version of a Hindu avatara story. It argues that in simultaneously affirming and challenging the religious beliefs that inform that story, the film maker raises some provocative questions about the vitality of religious and cultural traditions in the diaspora. The article concludes with a brief examination of how the film was received by audiences in India and in Europe and North America.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".