Impact valuations in social finance: emic and polyvocal stakeholder accounts
Bibliographic record
Abstract
Purpose This paper aims to understand how social finance and impact measurement experts include stakeholders' voices in valuations of social and environmental impact. Design/methodology/approach The paper used the content analysis of an online discussion forum where experts discussed impact valuation approaches. Findings Many experts seek impact valuations that take into account the experiences of those whose lives are most affected. Ideally, these accounts need to be emic to (in the language of) those stakeholders, and polyvocal (representing many different stakeholders' voices). However, these experts also seek to effect systemic change by encouraging mainstream financial markets to use social and environmental valuations in their decision-making. These experts consider full plurality too complex to be useable by financial markets, so the experts argue in favor of etic valuations (stated in the language of investors), to appeal to mainstream finance, while endeavoring nonetheless to represent multiple stakeholders' voices. The authors identify two discursive strategies used to resolve this tension: effacing of differences between diverse stakeholders, and overstating the universality of money as a common language. Social implications The terms emic and polyvocal provide experts with nuanced ways to understand “stakeholder voice.” The authors hope these nuances inspire new insights and strategies and help the community with their goal of bridging to mainstream finance. Originality/value The paper presents a theoretical framework for describing plurality in impact valuations and examines the challenges of bridging from social finance, which seeks to give voice and representation to those whose lives are most affected, to mainstream finance.
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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 | 0.044 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".