Cultural Mixers: Race, Space, and Intercultural Relations among Youth in East-end Toronto
Bibliographic record
Abstract
The objective of this study is to examine the way youth negotiate belonging in two priority neighbourhoods – Malvern and Chester Li – in Toronto’s east-end. It asks how youth experience belonging and negotiate difference in ‘priority neighbourhoods’. In what ways does space shape belonging and difference? In contrast to previous studies that are spatially decontextualized, I argue that neighbourhoods are the very sites where youth negotiate differences and connections as they engage with peers, families, friends and residents. The importance of space in studying youth’s sense of belonging is particularly valuable in Toronto where neighbourhoods are highly diverse and stratified. My work is inspired by Yuval-Davis’s (2006) notion of belonging and the politics of belonging and Bourdieu’s (1984) concepts of social field and habitus. I braid together a conceptual framework with the aim to achieve a more nuanced understanding of the ways power operates in the everyday context of ‘priority neighbourhoods’ and how processes of inclusion and exclusion and boundaries of belonging are demarcated.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".