Changing dynamics in the Canadian voluntary sector: challenges in sustaining organizational capacity to support healthy communities
Bibliographic record
Abstract
The voluntary sector is recognized, by citizens, industry and government, as an increasingly vital contributor to healthy communities within Canadian society, called upon to provide front-line service delivery in areas of community support that were in the past often served by government and or religious charity. (The voluntary sector is large, consisting of an estimated 180,000 non-profit organizations [of which 80,000 are registered as charities] and hundreds of thousands more volunteer groups that are not incorporated [Statistics Canada, 2002].) The dynamics of the sector have changed considerably over the past decade, as government has pulled back the level of core organizational funding support and the role of the church has diminished. As community health is directly related to the organizational health of service-providing non-profits and charities, these organizations are looking increasingly towards corporate and individual donors, along with new self-financing approaches that generate revenues. They are also facing challenges in attracting and retaining skilled and motivated volunteers. As the scope of the voluntary sector and its overall influence grows, so do the organizational and financial challenges it faces. This article will address in particular the issue of funding support for healthy communities and examine a number of potential and existing best practices for sustaining community health in Canada. We will also look at the issue of volunteerism and human resource capacity challenges for organizations. This is an area in which the Canadian government has decided to focus as a result of explicit policy decisions taken in the late 1990s.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.039 | 0.020 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".