Valuing a new “good”: Debates over the value of the social good in Canada's Social Finance Fund
Bibliographic record
Abstract
Abstract Despite promises of blending financial returns and social good to produce positive impact, what distinguishes “social” finance from the more traditional financial sector is ambiguous and often contested. Indeed, while financial returns are easily quantified and defined, the idea of a social return contains a plurality of understandings of how the social good is valued, who has power over its valuation, and its relative worth in comparison to financial returns. This article addresses this tension through examining how the concept of the social good has been understood and contested in the creation of Canada's Social Finance Fund. Using the creation of the fund in 2018 as a departure point, I outline the results of 37 interviews with social finance participants including representatives from social purpose organizations, intermediaries, and investors. Based on these interviews, I argue that struggles over how to value the social good hinge on the geographic imaginaries of those involved in the sector, power relations that privilege investors, and the models of organization which are embedded in existing investment systems. However, despite the relative power of investors, the need for social sector buy‐in allows non‐profits and service providers to influence how ideas of the social good manifest in valuation processes.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.032 | 0.041 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".