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Record W4256011409 · doi:10.32920/ryerson.14638653

Civic Participation of Visible Minorities in Canadian Society: the role of nonprofit organizations in Canada’s four most diverse cities

2021· preprint· en· W4256011409 on OpenAlexafffundabout
Agnes Meinhard, Farhat Faridi, Pauline O'Connor, Manveer Randhawa

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsToronto Metropolitan UniversityVictoria Park
FundersSocial Sciences and Humanities Research Council of CanadaMount Royal University
KeywordsMainstreamNonprofit sectorVoluntary sectorVotingPoliticsDemocracyPolitical scienceCivic engagementPublic relationsSocial capitalImmigrationPublic administrationPillarLaw

Abstract

fetched live from OpenAlex

Newcomer engagement or participation in the nonprofit sector has been shown to be an important pillar for upholding democracy, linked to voting behaviour and political participation; the creation of social capital; and enhancement of newcomer involvement in local decision making. This paper presents results of a study that focuses on two ways in which immigrant minorities have their interests represented in community decision-making: the first through the formation of ethno-specific voluntary organizations that represent their specific interests; the second via participation as leaders, board members and volunteers in ‘mainstream’ nonprofit and public organizations. Keywords: CVSS, Centre for Voluntary Sector Studies, Working Paper Series,TRSM, Ted Rogers School of Management Citation

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.386

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0190.004
Scholarly communication0.0050.001
Open science0.0020.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.021
GPT teacher head0.264
Teacher spread0.243 · 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 designObservational
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".

Quick stats

Citations2
Published2021
Admission routes3
Has abstractyes

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