Evidence‐based Engagement in the Voluntary Sector: Lessons from Canada
Bibliographic record
Abstract
Abstract The shift towards governance and greater reliance on third parties in the design, implementation and evaluation of policy has created new pressures to ensure that policies are designed and delivered in a consistent and effective manner. In the interest of improving transparency, accountability, effectiveness and efficiency, governments in Canada and in the UK, as in many industrialized countries, have begun to emphasize the need for evidence‐based policy‐making. As a result, knowledge and research have become key assets in the production of policy. Yet, with their current capacity and knowledge base wanting, governments have increasingly relied on the knowledge and information of external actors and have afforded greater authority to them on this basis. This has created a situation in which evidence‐based inputs are given greater weight. This shift has particular implications for voluntary sector organizations whose basis for intervention has lain historically with the interests that they represent. Already, in the Canadian case many national organizations have seen their focus shift to research activities under the impetus of new funding initiatives explicitly encouraging activities grounded in knowledge and policy analysis. Moreover, policy guidelines have been elaborated in order to enhance the sector's capacity to contribute to the development of policy in a depoliticized manner. Using a series of interviews conducted with representatives from national voluntary organizations in Canada, this article explores the implications of such a shift for the voluntary sector in Canada, and asks whether the Canadian case holds some lessons for voluntary sector–state relations in other jurisdictions.
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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 | 0.029 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.024 | 0.013 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| 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".