Interpreting the Signals: Change, Uncertainty, and The State of the Voluntary Sector in Canada
Bibliographic record
Abstract
While there has been much change in Canada’s voluntary sector during the 1990s and in the social, economic and political circumstances within which the sector operates, there has been no broad examination of trends in the sector nor any assessment of the overall state of the sector. Taking a wide-angle view, this analysis addresses a series of questions: What do recent studies suggest is the direction of change in volunteering and giving? What social dynamics underlie change in the sector? How uniformly are various trends distributed across the country? How robust is the sector’s resource base? What is happening to voluntary organizations? Where is the voluntary sector as a whole headed — has the wave crested? And how do trends in Canada’s voluntary sector compare with those in other countries? A review of more than 25 statistical measures shows with few exceptions a broad pattern of decline across nearly all aspects of volunteering and charitable giving, a pattern that holds in other countries as well. This pattern is countered by the fact that the influential civic core — the approximately one-quarter of the adult population that provides more than three quarters of the sector's support by individuals — is stable in size and has even increased its relative contribution; this suggests that claims the sector is increasingly fragile due to declines in participation and generosity should be
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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 | 0.015 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".