Bibliometric analysis of research on responsible investment
Bibliographic record
Abstract
The formation of the concept of responsible investment involves a change in the basic understanding of the investment process, which requires consideration of the possible consequences of such actions for the planet, society and economy. In this regard, it is important to provide a thorough methodological basis that will be the groundwork for the dissemination of this concept and its scientific foundation. The purpose of this work is to conduct a quantitative bibliometric analysis of research on responsible investing. The scientometric international databases Web of Science from Clarivate Analytics and Scopus from Elsevier and their built-in tools were used for this purpose. The time period of the study was 1990 – March 2021, the main search query – «responsible investment». Quantitative analysis of scientific publications in selected databases was conducted by time, geographical and subject search, analysis of organizations that fund research on this topic and the most cited works. The results show a growing trend of research on responsible investment in the world, with an increase in recent years, and a predominance of research by scientists from English-speaking countries (UK, United States, Australia, Canada) and European countries (Spain, France, Germany, etc.). Research is mainly funded by the European Commission and other Japanese and European organizations. The analysis of subject areas in the study of responsible investing revealed the presence of both managerial and economic, as well as social and environmental issues. The analysis of the most cited works in the scientometric databases Scopus and WoS revealed the popularity of socially responsible investments in the context of institutional, behavioral and functional aspects, as well as their connection with corporate social responsibility.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.035 | 0.088 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".