Bibliographic record
Abstract
For South Asian Canadians who migrated to Toronto in the 1970s, the only place for them to purchase and consume South Asian foodstuffs would have been in the area referred to as ‘Little India’, which later developed into what is referred to today as the Gerrard India Bazaar (GIB). Little India is located on Gerrard Street, encompassing the nine blocks from Greenwood Avenue to Coxwell Avenue. The very first South Asian entrepreneur in Gerrard Street was Gian Naaz, who rented the defunct Eastwood Theatre in 1972 and began showing films in Hindi and other South Asian languages. Naaz’s success inspired and attracted other South Asian entrepreneurs, some of whom opened restaurants and grocery stores. These early South Asian businesses on Gerrard Street combatted racism and racial stereotyping and the GIB was a microcosm of the violences South Asians experienced all across Toronto in the 1970s and 80s. As such, this paper tells the story of how South Asians, both them and their businesses, persevered and helped develop the GIB as an ethnic enclave because it allowed South Asians to affirm notions of home and belonging in Canada, all without ever having a distinct residential identity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.071 | 0.033 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.012 |
| 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".