Advancing the collaborative and democratic practices of policy innovation labs with community engaged scholarship
Bibliographic record
Abstract
‘Collaborative public sector innovation’ (CPSI) captures intersectoral collaborations aimed at using new ideas and approaches to solve complex societal problems. An important site of study in this area has been policy innovation labs (PILs), research and experimentation hubs that aim to improve policy outcomes by applying research evidence and facilitating intersectoral collaborations. Around the world, the number of PILs has grown rapidly in recent years, as has their study. Nevertheless, there is still limited understanding of their structures and operations, including the roles of collaborators and the nature of collaborations they support, their strategies for engaging with diverse residents, their potential impacts on the policy space, and the extent to which their design might also advance democratic innovations. In this article, we use a case study of a policy project hosted recently by a PIL in a mid-sized city in Canada to argue that using a community engaged scholarship methodology in a PIL can address knowledge gaps related to practicing CPSI and contribute to overcoming barriers to democratic innovations.
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 | 0.099 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.019 | 0.059 |
| Scholarly communication | 0.029 | 0.028 |
| Open science | 0.006 | 0.050 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".