Bibliographic record
Abstract
A prominent characteristic of the city of Toronto is its increasing diversity, with half of the city’s population being foreign-born. While the concept of diversity appeals to Toronto’s reputation as a multi-cultural haven, the city’s approach to managing diversity is becoming increasingly instrumentalist, i.e. diversity is considered an asset as long as its benefits are economically valuable. As a result, inner-city neighbourhoods in Toronto are thriving due to development projects and services, while the most diverse neighbourhoods in the inner-suburbs are left in a dire state. This article presents an analysis of how the concept of diversity used within policy euphemises systemic discrimination and inequality based on race, class and gender. It serves to reveal the mismatch between policy rhetoric on diversity and its materialisation in the daily lives of the inhabitants of a low-income Toronto innersuburb, by juxtaposing policy discourses with inhabitants’ everyday experiences. By illustrating how inhabitants reproduce negative essentialised stereotypes based on diversity markers, the article argues that talking diversity as an alternative to or an escape from problematising the intertwined systems of race, class and gender oppression, could potentially serve to perpetuate them.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.090 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".