Civic Engagement, Social Cohesion and Social Integration in Toronto,Canada
Bibliographic record
Abstract
The voluntary sector has long been seen as the foundation of a healthy civil society (DeTocqueville, 1961; Leonard & Onyx, 2003). Yet, substantial growth in the last two decades in demand for voluntary sector services in Canada has been accompanied by a significant reduction in government resources supporting the sector’s activities (Browne, 1996). This confluence of demand growth and decreased governmental support has resulted in increased competition among voluntary organizations for both capital and human resources (Meinhard & Foster, 2000). Furthermore, the ethnic transformation of Canadian society has raised knowledge, policy and practical issues across all sectors, including the voluntary sector. These conditions have pushed many in the voluntary sector to reach beyond their traditional bases of support to consider hitherto untapped segments of society, and have pushed governments to rely more and more on the voluntary sector for the development of social integration. However, research on the Canadian voluntary sector, particularly with a cross-cultural lens, is a relatively new research domain, with many gaps in the knowledge base. As a starting point, Berger (2004) and Berger & Azaria (2004) have proposed, tested and supported a framework that traces the relationship between sub-group identity and volunteering, as mediated by attitudes, norms and social barriers. In this paper we extend this framework and consider the role of civic engagement in processes of social cohesion and social integration. We use the 2002 Ethnic Diversity Survey (EDS) to investigate how engagement in the voluntary sector contributes to the development of both bonding and bridging social cohesion, and thereby, social integration. Keywords: CVSS, Centre for Voluntary Sector Studies, Working Paper Series, TRSM, Ted Rogers School of Management
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.016 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".