Delineating Impact Investing: A Bibliometric Review
Bibliographic record
Abstract
Nomenclature like impact investing, ethical investing, and responsible investing have grown quickly in recent years as investors explore the nexus between financial, environmental, and social returns. However, a common topic of debate in both academic and grey literature is establishing definitional clarity around the motivations and applications of each form of investment strategy. Adopting recent advancements in quantitative textual analysis and bibliometrics this study explores the current status of these related but disparate fields of research, by analyzing the metadata of 929 publications. We argue that impact investing, due to its emphasis on intentionality, multi-criteria decision-making, and incommensurability of values, is particularly well suited to address grand societal challenges like poverty, well-being, and climate change. However, impact investing remains a nascent subfield of social finance. We conclude that impact investing research should adopt bespoke theoretical frameworks to advance the field of study.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.017 | 0.044 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; 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".