The Influence of Outcomes-based Funding Models on Two Large Canadian Non-profit Organizations
Bibliographic record
Abstract
Through a qualitative case study of two large non-profit organizations, the purpose of this research was to understand and explore how non-profit organizations are being influenced by outcomes-based funding models. During the last two decades, there has been increasing pressure on non-profit organizations to demonstrate the impact of their programs, often as a condition for receiving funding from the government and non-government funders. Traditionally, non-profit organizations have been delivering programs through staff and volunteers (inputs) and measuring success through the number of individuals participating in the programs (outputs). The mission and vision of non-profit organizations are often broad and to assess the organizations’ success by solely measuring the number of participants in a program has been questioned for some time thus leading to a shift towards outcomes measurement process. The outcomes measurement process is a type of evaluation that is focused on measuring how the programs impact the lives of participants lives rather than what the program does for the participants in terms of delivery of activities. More recently, both government and non-government funders have changed their funding models to be outcomes-based and requiring organizations to demonstrate short-term, medium-term or long-term impact of their programs on the participants. The findings of this research study indicate that there is a need for further collaboration and open communication between funders and the non-profit organizations to resolve the tensions between the purpose of outcomes-based funding models and the capacity needed to meet those funding requirements. The study also highlighted the need for different funders to collaborate with one another to support the non-profit organizations in their work on outcomes measurement for measuring short-term, medium-term or long-term impact of their programs. The organizations too need to take leadership on the outcomes-measurement work for their own organizational learning, strategic thinking and planning. While the findings of this study cannot be generalized, they are suggestive and have implications that will be of interest to anyone working in the non-profit sector and not only to funders and leaders of non-profit organizations.
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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 | 0.027 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.041 | 0.016 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| 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".