Closing the Loop: Corporate Links to the Voluntary Sector
Bibliographic record
Abstract
This paper brings together findings from three separate investigations to provide a deeper understanding of the changing roles of the government, for-profit and nonprofit sectors in ensuring civil society. The first study, based on a survey of 645 nonprofit organizations from across Canada, revealed a nonprofit sector changing to meet the challenges of the times, despite a general pessimism among leaders of nonprofit organizations as to their future (Meinhard & Foster, 2003a & b). The second, based on interviews with 20 Government of Ontario officials with links to the nonprofit sector, demonstrated how civil servants struggled to help nonprofit organizations adjust to the new policies and also encouraged them to form partnerships with the for-profit sector (Meinhard & Foster, 2003c). The research reported in this paper, based on interviews with 17 senior officers of Ontario-based corporations active in philanthropy, focuses on the corporations and probes more deeply in to the myriad of ways they are getting involved in their communities as socially responsible corporate citizens. The findings from the corporate interviews are compared and melded with those from previous interviews with government officials and nonprofit organizations to provide a three-dimensional perspective of the direction in which Canadian civil society may be moving. 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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".