Impact investments: a call for (re)orientation
Bibliographic record
Abstract
Abstract Practitioners and academics have been using different terms to describe investments in the sustainability context. The latest inflationary term is impact investments—investments that focus on real-world changes in terms of solving social challenges and/or mitigating ecological degradation. At the core of this definition is an emphasis on transformational changes. However, the term impact investment is often used interchangeably for any investment that incorporates environmental, social, and governance (ESG) aspects. In the latter instance, achieving transformational change is not the main purpose of such investments, which therefore carries the risk of impact washing (akin to “green washing”). To offer (re-)orientation from an academic perspective, we derive a new typology of sustainable investments. This typology delivers a precise definition of what impact investments are and what they should cover. As one central contribution, we propose distinguishing between impact-aligned investments and impact-generating investments. Based on these insights, we hope to lay the foundation for future research and debates in the field of impact investing by practitioners, policymakers, and academics alike.
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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 | 0.023 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.036 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".