Bibliographic record
Abstract
Can You Hear the Nightbird Call? Anita Rau Badami. Toronto: Knopf, 2006.412 pp. $34.95 ho. The tragedy of Air India Flight 182 remains the worst act of terrorism in Canadian history. Over twenty years after the bombing, the questions left unanswered are still making headlines. Anita Rau Badami's novel Can You Hear the Nightbird Call? has been marketed as a story about this disaster, which on one level it is; however, the novel as a whole is an immigrant family saga that traces the tale of two families, one Sikh and one Hindu, whose lives are shattered by countless violent events that ensue from the partition of Hindu India from Muslim Pakistan in 1947. Although the novel is saturated in sadness and misfortune, Badami's raw language and evenhanded tone steer the story away from overwhelming drama and situate it within an honest reality. The originality of Badami's novel does not reside in her portrayal of the horrific actions surrounding the political tensions in India, which in many ways echo Rohinton Mistry's A Fine Balance; the uniqueness of her story dwells in her focus on the effects of the events on Indian families in Canada. In writing from this angle, Badami not only positions herself as an author interested in the narratives of immigrants in a multi-culti country (p. 266), but she also establishes the tragedy of the Air India case as a Canadian catastrophe. Badami's vast tapestry of tales is divided into five sections. The first three introduce the reader to the characters' separate, compelling histories, which unravel with the breaking down of India's national boundaries. The opening portion centers on Bibi-ji, a poor but beautiful young Sikh girl from Punjab who uses her arresting looks to steal her sister's betrothed and thus move to Vancouver in hopes of a life filled with extravagances, such as lavender soap and chocolate (p. 27). As the novel progresses, Bibi-ji becomes representative of an in-between space between Indian and Canadian cultural identity. She convinces her husband, Pa-ji, to open an Indian restaurant called The Delhi Junction, where they provide new arrivals with advice on visa and immigration procedures (p. 61). Paji later builds a house that Bibi-ji calls the Taj Mahal, where immigrants can stay until they are properly settled. Both the restaurant and the Taj Mahal become areas of political strife as the enmities generated by distant homelands (p. 67) produce divisions within the Indian communities in Vancouver. Leela's half-and-half story founds the second part of Badami's family chronicle (p. 73) and further exemplifies the overarching theme of in-between spaces. …
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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 | 0.001 | 0.001 |
| 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".