The balancing act: community agency leadership in multi-ethnic/multiracial communities
Bibliographic record
Abstract
Toronto has become one of the most diverse cities in North America, and certainly the most diverse in Canada. In 2004 Toronto had the second-largest number of foreign-born residents of any major world city (UNDP, 2004). By 2011, nearly 50% of the total population were ethnic/visible minorities, with the top five visible minorities quickly becoming the ‘visible majority’; these included South Asians, making up 12.3% of the population; Chinese, 10.8%; Black, 8.5%; Filipino, 5.1%; and Arab/West Asian/Afghan (that is, Assyrian and Iranian), 3.1% (Statistics Canada, 2011). In such an ethnically diverse environment, challenges are inevitable. Since ethnicity is a contested term with varying ontological and epistemological challenges I will borrow Varshney's definition to nail down how I intend to relate to the term. By ethnicity I mean a ‘term which designates a sense of collective belonging, which could be based on common descent, language, history, culture, race, or religion (or combination of these)’ (Varshney, 2007, p 277). The communities on which this chapter focuses are nestled within two of the most diverse federal political boundaries in Canada – York West and York South Weston – in Northwest Toronto. In York West, over 72% of the population is non-white, while in York South Weston over 50% of the population is non-white. In York West, the top three ethnic groups proportionately are White (27.5%), Black (22.4%),and South Asian (16.0%). In York South Weston the top three groups are White (44.9%), Black (21.1%) and Latin American (9.0%) (City of Toronto, 2011). Within each of these political boundaries are three communities, whose real names have been altered, which we will call Days, Chalks and Falls. Days is ethnically dominated by South Asians; Falls comprises predominantly Somalis and Black/Afrodisaporic Caribbean peoples; and Chalks comprises predominantly Spanishspeaking peoples. This categorisation of course begs the question as to whether or not Somalis are Black and requires deeper analysis than I can provide here. This classification is based solely on self-segregation of peoples in the communities. Although the primary focus of the chapter will be on my work with these communities, I will draw on community work in other communities in Toronto, as I have similar experiences throughout. These communities are part of, or very close to, communities identified by the city of Toronto as Neighbourhood Improvement Areas (NIA), classified as such because of the lack of resources and levels of poverty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".