Social Innovation Labs: A Neoliberal Austerity Driven Process or Democratic Intervention?
Bibliographic record
Abstract
Social Innovation Labs (SILs) are a recent policymaking development that are spreading rapidly in many different countries. SILs are said to address difficult and complex social policy problems that have been resistant to solutions. To date, there has been limited scholarly analysis of SIL development, with many questions in need of critical policy assessment. This paper seeks to conceptualize SILs in the Canadian context by mapping the sector and exploring how these labs fit within the broader ecosystem of policy innovation. We consider why SILs have become so popular in this particular socio-political moment. We contend that the SIL trend speaks to a dual and contradictory desire on the part of governments for more participatory policymaking and cost saving. Thus, while SILs may create opportunities for the democratization of social policy, they are also motivated by efforts to do more with less in an environment shaped by austerity and neoliberalism. This suggests that SILs could equally result in the marketization and depoliticization of social policy. This paper highlights these tensions conceptually with the purpose of guiding empirical studies that explore how these contradictions may manifest in policy practice and perhaps offer openings for policy that addresses both the roots and symptoms of complex social policy 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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.083 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".