The Deployment of Partnerships by the Voluntary Sector to Address Service Needs in Rural and Small Town Canada
Bibliographic record
Abstract
Abstract Service restructuring trends since the 1980s have resulted in the downsizing or closure of many services in rural and small town Canada. In response, voluntary groups have been filling some of the emerging service gaps. Services, however, often are directed at complex problems that demand information, support, or assistance from a range of sources and institutions. For voluntary groups, this underscores a need to partner with other groups, organizations, or service providers. At the same time, voluntary organizations are increasingly encouraged to develop partnerships with public or private partners in order to qualify for government funding. This study tracks 29 voluntary organizations in four rural and small town places across Canada to explore the development and maintenance of partnerships (both local and non-local), as well as the types of networks, resources, and expertise for which partnerships were used. The findings indicate that while voluntary organizations feel that local partnerships are more important, partnerships with groups outside of these places are equally developed. Partnerships were used to expand networks, obtain expertise, and access a range of resources to assist in daily operations and delivery of services. The increase in partnerships with groups outside of these communities, particularly with non-local service providers, will have important implications for voluntary organizations and policy makers.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".