The ecology of open innovation units: adhocracy and competing values in public service systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There have been concerted efforts to encourage innovation and to foster a more innovative and “open” culture to government and public service institutions. Policy and service innovation labs constitute one part of a broader “open innovation” movement which also includes open data, behavioral insights, digital services, data science units, visualization capabilities, and agile and lean methods. This article argues that we need to step back and better understand these “ecologies” of innovation capabilities that have emerged across public service institutions, and to recognize that as fellow “innovation” traveling companions they collectively seek to transform the culture of government and public service institutions, producing more effective, efficient and tailored policies and services. This article introduces analytic frameworks that should help locate policy and innovation labs amidst these other innovating entities. First, it delineates the various units and initiatives which can be seen as committed to new ways of working and innovating in public service institutions, often relying on “open innovation” rhetoric and approaches. Second, it shows how – despite the diversity among these entities – they nevertheless share similar attributes as “adhocracies” and are located as part of a broader movement and class of organizations. Third, we locate these diverse OI entities amidst broader public service systems using the Competing Values Framework. Fourth, this article situates the challenges confronting OI units developing and sustaining or broadening niches in public service systems. Finally, it identifies future research questions to take up.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it