Bibliographic record
Abstract
The current study intends to understand the spectrum of existing scientific literature on Impact Investing, explore the publication credentials of that literature in the form of bibliometrics, and investigate the future scope for research in the area of impact investing. We have used the visualization tool VOSviewer to analyze the bibliometric data collected from the Scopus database. ‘Sustainable development’ was identified as the most used keyword in the documents, followed by ‘corporate social responsibility’, ‘investment’, and ‘environmental performance’. The United States was the leading contributing country followed by the UK, China, Italy, Canada, and India. Based on the systematic review of 753 journal articles, we have identified five distinct research areas in the field of impact investing. The top five research clusters are sustainable development research, sustainability research, corporate social responsibility research, Sustainable investment research, and environmental economics research. Our results revealed a dearth of focus among researchers in identifying impact investing either as a standalone concept or as a concept that drives better performance in organizations. Hence, we propose some promising areas of impact investing research. They are sustainable finance, firm performance, tourism, and climate change research. The output of this research has implications for researchers, practitioners, policymakers, and academia since it pinpoints the popular and promising clusters of research in the field of impact investing. Our study is unique and original in the sense that it covers a more comprehensive inclusion, and exclusion criteria as well as keyword combinations and thus, it provides better directions for future research in the field.
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 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 | 0.014 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.316 | 0.283 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".