Government and Non-profit Collaboration in Times of Deliverology, Policy Innovation Laboratories and Hubs, and New Public Governance
Bibliographic record
Abstract
Abstract This article investigates the implications of the move from public administration to new public management to new public governance for relations between the state and non-profit organizations using the example of the development of policy hubs and innovation laboratories under the operational theory of deliverology. Much of the literature suggests that the move towards these collaborative arrangements is providing non-profits with more access and influence in the policy process. Another stream suggests that the changes may be less significant and less positive than assumed for non-profits. This article weighs in with a preliminary examination of policy hubs and innovation laboratories in Canada. It confirms that while collaborative arrangements between the two sectors are expanding and increasingly drawing non-profit actors into the centre of policy-making, non-profit organizations may be wise to heed certain cautions when choosing their partners and terms of the partnerships or they may find their ability to create and influence policy in a meaningful way is limited.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".