Bibliographic record
Abstract
中国影响力投资市场有什么独特之处？在中国做影响力投资需要如何创新？针对这两个问题，禹闳都有资格回答。禹闳是唐荣汉在中国创建的一家影响力投资先驱。在中国做影响力投资，不仅要追求社会影响力目标，也要追求市场化的投资回报率。为了同时实现此双元目标，禹闳首先基于联合国十七项可持续发展目标选定了三大投资主题；接着，确立了六条选择标的原则；在投资中，禹闳还向企业提供赋能服务，帮助其创造双元价值；同时，禹闳还打造了一套影响力衡量和管理体系。然而，这些举措仍然不能保证禹闳成功实现双元目标，因为它还要面临投资退出挑战。2022年8月，它对馨桐的投资就面临了这个难题。它遇到了两个意向投资者，A可以带来较高的投资回报率，但可能会引起馨桐使命漂移；B能带来的投资回报率有限，但引起使命漂移的可能性也较小。该选择谁呢？唐荣汉难以抉择。
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.061 |
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".