Counting on Each Other: A Social Audit Model to Assess the Impact of Nonprofit Organizations
Bibliographic record
Abstract
Betty Jane “B. J.” Richmond Ontario Institute for Studies in Education, University of Toronto The purpose of this study is to shed light on the social and economic impact of nonprofit organizations on their communities. The literature on nonprofit organizations in Canada and the United States offers few frameworks from which to study nonprofits’ social-economic role or their value. This study examines nonprofit organizations as part of a social economy that also includes cooperatives and mutual benefit organizations. The social economy perspective promotes an emphasis on the social and economic functions of organizations with a social purpose. Exploring the current separation of market and social value, as well as alternative theories of value, this study develops a model of how nonprofits produce social capital. A labor theory of value is adapted to explain how nonprofits create surplus value. As well as a theoretical model, the study develops a practical social audit model for assessing a nonprofit organization’s participation in the social economy of its community. The model combines a financial audit approach with other evaluation techniques and is designed for use in all three economic sectors. Identifying the social and economic resources that enter the organization in an audit year and those that return to the community, the model puts forward methods for assessing an organization’s outcomes. It then attributes a comparative economic value to them. The social audit model is tested on a community-based employment-training agency serving persons with disabilities and other severe barriers to employment. The findings demonstrate that the agency’s social return-onNonprofit and Voluntary Sector Quarterly, vol. 29, no. 4, December 2000 594-595 © 2000 Sage Publications, Inc.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".