Toronto's Little India: A Brief Neighbourhood History
Bibliographic record
Abstract
Preface: This historical survey of Toronto’s Little India neighbourhood began in November 2009, with an email circulating among faculty members of Ryerson University with research interests in immigration and settlement. In this email, Laura Thomas of the Riverdale Immigrant Women's Centre (RIWC) requested assistance to conduct a neighbourhood survey of the Gerrard-Coxwell area: “We are located in such a diverse cultural and physical landscape, and are interested in understanding our history of immigrant settlement patterns”. The ultimate aim would be to provide the community with historical, geographical, social and cultural information as a resource for local revitalization and economic development, community engagement and support of the arts and environmental groups. In an initial meeting with Nuzhath Leedham, the executive director of RIWC, Harald Bauder learned about a “cultural mapping project” that was conducted earlier in the year in Chinatown, and which RIWC sought to replicate for the Gerrard-Coxwell area. After a closer look at this project, it became clear that replicating Chinatowns’ “cultural mapping project” was not feasible, given the resources available and university-based constraints on research ethics. Instead, we decided to conduct a more “conservative” study of Little India’s neighbourhood history. Although the term “mapping project” continued to circulate through the community to describe this survey, our project is more about compiling and summarizing a neighbourhood history. As a resident of the Little India neighbourhood since November 2008, Harald Bauder’s contribution to this report is inspired by William Bunge’s (1971) book Fitzgerald, a groundbreaking work of humanist geography, presenting the history, development and contemporary challenges of a particular neighbourhood of Detroit. Angelica Suorineni spent countless hours in the library and archives as a graduate student researcher. Although it would be preposterous to claim that this brief report could be of similar sophistication, scope or impact as Fitzgerald, we hope, like Bunge, that it will serve the community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.014 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".