Bibliographic record
Abstract
This chapter explains and discusses different innovation typologies. “Typology” refers to not only types of innovation but also other aspects. This chapter explains and discusses innovation types, including product or service (e.g., using online system by public organizations), process (e.g., one-stop-shops), mission (e.g., get to the Moon), policy (e.g., energy), partner (e.g., collaborating with business and nonprofit organizations to deliver services), citizen (e.g., codesigning parks with citizens), technological (e.g., new online car registration), social (e.g., providing affordable housing), governance (e.g., citizen participation apps), marketing or communication (e.g., promotion of public services), and rhetorical innovations (e.g., a new logo, without changing organizational structure) in the public sector. In addition to these innovation types, this chapter also discusses other typologies and aspects, including the radical (or breakthrough, e.g., Open University), incremental innovations (e.g., non-disruptive innovation), complex (e.g., introducing a toll charge in a road), and open innovations (e.g., civic hackathons) in the public sector. This chapter offers insights into how these distinctions matter for innovative activities.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".