Scaling Impact Investment for Sustainable Development Goals: An Empirical Analysis
Bibliographic record
Abstract
Impact Investing is a community of investors willing to create social and environmental impact along with financial returns by investing either directly with Base of Pyramid[1] (BoP) enterprises or indirectly through enterprises that help in creating impact by investing in BoP organizations. Adoption of SDGs[2] quantified the expectation paradigm of the global community for social, environmental and economic achievable and projected/targeted achievement of SDGs by 2030 made the governments, businesses, institutions daunted with the task in hand hence, it is imperative for investing community to contribute its share as well. With high social need and underserved population India has become a test bed for impact investing. However, with increasing impact investing, Impact Measurement and Management (IMM) gains significant importance as it allows investors to evaluate impact and channelize fund to most effective solutions. The present study conducted for year 2019 not only attempts to explore impact investing landscape in India and its future dimension but it simultaneously does content analysis of impact report of investors using impact value chain[3] and indicators developed on the basis of SDGs targets and indicators. The analysis aims to establish a link between developed indicators and impact, the link once established, developed indicators will provide agile, cost effective, quantifiable and measurable basis to impact that has worldwide acceptance. [1]Base of Pyramid refers to the poorest two-third of the economic human pyramid living in abject poverty. [2]SDGs, adopted in 2015 by all UN member states, are universally accepted goals and targets under goals to guide sustainable development and create a sustainable world for all. [3]Impact Value chain is a tool build on theory of change to illustrate how enterprise activities lead to desired outcome and impact by setting a relationship between activities, output, outcome and impact.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".