Drivers and Conditions for Innovation
Bibliographic record
Abstract
This chapter focuses on how governments, public organizations, and public sector employees and managers can be more innovative. In other words, the motivating question is: What are the drivers and conditions for innovations in the public sector? Conditions for innovation are also essential because public sector employees, employees’ work groups, public organizations, countries, and international and supranational organizations must innovate. Thus, an important question becomes how and why individuals, groups, organizations, countries, and international organizations achieve innovations. What are the conditions for innovation? Answering this question is vital because it explains how governments (at national, regional, state, and local levels), organizations, groups, and individuals can innovate when there are the right conditions. In other words, based on the context and actors’ involvement, public organizations may require different conditions to innovate. This chapter discusses drivers and conditions of innovations at the national, organizational, workgroup, and individual levels.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 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".