A framework to conceptualize innovation purpose in public sector innovation labs
Bibliographic record
Abstract
Public sector innovation labs (PSI labs) are a rapidly proliferating experimental response to the growing complexity and urgency of challenges facing the public sector. This research examines ways in which PSI labs are currently being conceptualized in relation to their values, purpose, ambition, definitions of innovation, methods, and desired impacts. Distinctions between PSI labs that work within dominant systems and paradigms to make them more efficient, effective, and user-oriented and PSI labs that have a more transformative intent, are made and problematized. This research used a constructivist grounded theory and participatory action research methodology, working with lab practitioners as well as with literature, to build a framework to support stronger conceptualization of PSI lab purpose and intended impact. This framework provides a structure for researchers and practitioners to engage in richer description, thinking, and comparison when designing, studying, and evaluating PSI labs. Although this research focused on labs in the public sector, the findings and framework are relevant to other types of innovation labs working in multiple sectors.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.006 | 0.050 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".