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Record W4299635939 · doi:10.46692/9781447333753.020

The balancing act: community agency leadership in multi-ethnic/multiracial communities

2017· other· en· W4299635939 on OpenAlexaboutno aff
Morris Beckford

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsAgency (philosophy)Ethnic groupPolitical sciencePublic administrationSociologyLawSocial science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.145
Threshold uncertainty score0.287

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0330.038
Scholarly communication0.0180.011
Open science0.0020.012
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.456
GPT teacher head0.408
Teacher spread0.048 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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