Gentrification of ethnic businesses in Toronto's Little India: an analysis of ethnic business development
Bibliographic record
Abstract
Gentrification of ethnic businesses within ethnic economies is a new phenomenon that is vastly affecting Toronto’s Little India. As a result, research focusing on this issue and analysis on the way in which ethnic businesses have been developed is an important problem to investigate. This research will focus on three main research questions; observing the different looks, practices, styles, and tastes prevalent amongst restaurants in Little India, examining if the styles and tastes projected by restaurants’ ethnic habitus have an impact on how ethnic businesses fare, and assessing if entrepreneurs are able or unable to modify their business practices, and styles. The analysis will be conducted through the lens of the Habitus (Bourdieu, 1987) as a theoretical framework, specifically examining the ways in which self-employed migrants develop their businesses and the role that their ethnic background and culture may have in this process. This research will take an ethnographic methodological approach in conducting the research through two steps, beginning with a naturalistic observation of two restaurants and following up with interviews. The findings determined that slight changes made to the business approach and cultural habitus of ethnic businesses can prove successful in attracting the needs of the surrounding clientele and the gentrifying population. Keywords: gentrification, South Asian, Little India, ethnic economy, self-employed migrants
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".