Missing finance in social impact bond research? A bibliometric overview between past and future research
Bibliographic record
Abstract
Abstract This paper provides a bibliometric review of 156 articles published between 2011 and 2021 on social impact bonds (SIBs). We identified five research streams, namely studies that: (i) place the origins of SIBs in the neo‐liberalism framework; (ii) consider SIBs as an evolution of the new public management approach; (iii) focus on conceptualizing SIBs as an impact investment approach rooted in the social finance landscape; (iv) look at SIBs as a funding source for social entrepreneurship; and (v) detect an emerging phenomenon labeled as sustainable financial partnerships for the SDGs. Our results suggest that the current literature is strongly based on those that we have defined as a sort of UK influence, which is dominating the scientific perspective and the current use of SIBs, and that there is still less “finance‐based” research in this field. We conclude by proposing areas for future research.
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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 | 0.038 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.180 | 0.283 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".