Leping Foundation: Introducing Venture Philanthropy to China
Bibliographic record
Abstract
The case explores the challenges and opportunities of practicing venture philanthropy in an emerging market. Jaff Shen, a serial social entrepreneur and Founder/General Secretary of Leping Social Entrepreneur Foundation ("Leping") brought the international venture philanthropy network Social Venture Partners ("SVP") to China in 2012 to help build China’s overall civil society and proclivity for social entrepreneurship. SVP's Beijing branch was launched as a non-profit in 2013 and attracted thirty partners who invested in four education-related social enterprises within the first two years of operation. SVP Shanghai followed in 2016, registering as a for-profit company for greater flexibility in the context of China’s strong non-profit regulations. Given some freedom to develop its own path, SVP Shanghai evolved more slowly than Beijing, enrolling 11 partners within the first year without making any investments. Students are asked to explain the reasons for Shanghai's slow growth, and then critically evaluate a strategy suggested to Shen for accelerating the progress. They must take a stand in Shen's internal debate about whether to push a fast top-down strategy or tolerate a slower-paced bottom-up trajectory. Students also have the opportunity to compare four different SPO investment opportunities shortlisted by SVP Shanghai's partners for their initial investment, and to propose an evaluative framework for screening the potential targets and subsequently monitoring the investment outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.028 |
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; both teacher heads agree on what is shown here.
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".