Indicators for Public Sector Innovations: Theoretical Frameworks and Practical Applications
Bibliographic record
Abstract
The paper maps and analyzes all existing practical exercises aiming to develop indicators for public sector innovations. To our knowledge this is the first attempt to comprehensively gather information about various international efforts. We only considered such exercises where actual indicators were developed and used at least once. We map five such exercises through extensive desk research and 13 interviews with surveyed project members. The paper shows that all existing attempts to measure public sector innovations operate within a rather limited conception of the public sector (efficiency), neglecting other possible logics (e.g. legitimacy); the existing exercises also neglect large areas of public sector activities, e.g. cooperation with business and third sectors (such as service co-creation, public-private partnership practices). This narrow focus often dictates that indicators and their technical assumptions are copied from the private sector; none of the five analyzed exercises utilized public administration experience and research (e.g. on performance measurement). The paper argues that instead of trying to come up with quantified indicators, public sector innovations should be assessed in complex evaluation frameworks.
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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 | 0.026 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".