Building the de-risking state: power, policy and Canada’s Social Finance Fund
Bibliographic record
Abstract
The Canadian social finance market received a significant boost in 2018 when the Federal Government announced a $755 million (CAD) Social Finance Fund (SFF). Despite rhetoric painting private finance as a funding solution for austerity-stricken social programmes, the SFF was explicitly designed to use public capital to support an existing private market where, in the words of the Canadian government, ‘supply and demand are not meeting each other’. While details remained sparse at first, in early 2023 it became clear that the SFF would hand off responsibility for the SFF to private financial actors by transferring hundreds of millions to three firms tasked with subsidising private investment in social finance.In doing so, the Canadian government fit the wider trend of using public financial power to backstop private investment in areas deemed state priorities, referred to as the ‘de-risking state’. This article explores how a ‘de-risking consensus’ was built in a sector oriented towards social values rather than financial logics. We argue that studying how market institutions are constructed through acts of policy can highlight how power-laden policy networks and stylised best-practices are used to promote and justify de-risking market structures.
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 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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.025 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| 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".