Government under pressure: investing in better outcomes through social impact bonds
Bibliographic record
Abstract
With shrinking resources and declining federal transfers, provincial governments across Canada are forced to provide increased levels of supports to vulnerable individuals with decreasing resources (Janssen & Estevez, 2013). Governments continue to face obstacles in meeting the needs of vulnerable populations such as children, single parents, and those who are homeless, to name a few. Manitoba, for instance, faces demographic challenges related to an influx of newcomers who are seeking refuge, resettlement, and housing supports, an aging baby boomer population that will need end-of-life supports, as well as a growing number of children in government care. Instead of funding programs based on their activities and outcomes, this paper presents outcomes-based financing, such as the social impact bond, that reward service providers who are able to demonstrate proof of outcomes and can show how the intervention improved the lives of the individuals it was meant to serve. Under a social impact bond, government engages non-traditional partners in the private and non-for-profit sectors, and the community as a whole becomes part of the solution to challenging social problems.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".