Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Could impact investing be a catalyst for change and provide a bridge from profit maximisation towards positive corporate purpose? How might the financial community, in partnership with governments, regulators and entrepreneurs, contribute to the responses to the enormous challenges we face globally? Focusing on impact investing this chapter describes the features of this type of investing and how it has rapidly grown and considers its relevance for corporate purpose, sustainability and social benefit as part of a ‘Spectrum of Capital’. The chapter first seeks to provide some clarity around the meaning of impact investing and then locates impact investing along a ‘Spectrum of Capital’ with focus on the intentions of the investors and the types of projects they fund. Then the chapter looks at how the policy and regulatory framework that supports impact investing is evolving. There are certain features that may provide incentives for impact investing but, despite its appeal, there also some ‘blockers’ and challenges that could limit the potential contribution. The chapter concludes with some predictions and suggestions for the future of impact investing.<br/>
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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 it