Bibliographic record
Abstract
Malvern, a neighbourhood in Toronto, Ontario, was turned into a designated area for affordable housing during its transformation into a modern community in the late 20thcentury. Any positive connotation that was once attached to ‘affordable housing’ as an idyllic space for hard-working residents quickly disappeared, however, and Malvern has repeatedly been labeled one of Toronto’s most dangerous neighbourhoods, in dire need of improvement. In this essay, I borrow from Omi and Winant (2015) to argue that the neighbourhood of Malvern is a racial project – that is, Malvern’s representations assign meaning to race, created not only through racist and classist planning, but also through the ways that Malvern is shared in the larger public, through media representations of Malvern, and the complex experiences and realities of its residents. Populated almost entirely by visible minorities, the mapping of criminal deviance alongside racialized individuals has ensured that Malvern and its residents continue to be marred by stigma and stereotypes, leaving residents feeling conflicted with internalized and arguably perverse understandings of themselves, and without the necessary support that disadvantaged neighbourhoods should receive. Today, Malvern is the product of purposeful, structural violence, with the people of Malvern perceived as lacking the civility to maintain the ideal space that was created for them. Using the work of Henri Lefebvre, this paper provides a detailed analysis of the way that Malvern was conceived and perceived to exist and the way that it continues to be lived as a racial project. Malvern, like other inner-city neighbourhoods in North America, has remained at a disadvantage since its inception. In this essay, I explore how the perception of Malvern came to be and how first-hand experiences within Malvern’s borders differ from those which are negatively portrayed in the media.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".