Public sector innovation and wicked problems in Asia: From reactive to proactive governance
Bibliographic record
Abstract
The COVID-19 pandemic has become a catalyst for governments to break away from complacency and innovate at unprecedented speeds. By analyzing 15 cases of public sector innovation (PSI) in response to the pandemic, using the OECD Framework on Facets of Innovation across India, Singapore, and South Korea, we find that the crisis clearly pushed public sector organizations (PSOs) across Asia away from top-down and incremental approaches to PSI toward bottom-up and transformative approaches. We also find that countries adopt innovation facets differently based on their existing PSI capabilities, which we conceptualize as PSI maturity. Countries with less PSI maturity tend to innovate along the Reactive Axis, which extends between the Mission-oriented and Adaptive Innovation facets, whereas countries with higher PSI maturity tend to innovate along the Proactive Axis, which extends between the Enhancement-oriented and Anticipatory Innovation facets. We also find that collaboration and digitalization were key catalysts for PSI across all 15 cases studied. The article highlights limitations and areas for further research, and concludes with policy directions to help PSOs better equip themselves for future crises.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".