Outcomes of Public Sector Innovation
Bibliographic record
Abstract
This chapter analyzes the influences of the disparate impact of public sector innovation. It is one thing for a public sector organization to innovate but quite another for that innovation to have an unequivocally positive impact. If we consider innovation as an ecosystem, there are inputs, actors, and processes, and there should also be outputs and outcomes. Innovation for the sake of innovation will not work, so we need to consider and analyze particular effects, such as benefits, outputs, and outcomes, both in the short and long term. We can also connect the outputs and outcomes of innovations and features such as the context, sources, conditions, and barriers to innovation. For example, an innovation may have different outputs and outcomes in different contexts, and one source of innovation (e.g., bottom-up innovations) may bring about more positive benefits to organizations under certain conditions (e.g., more resources). This chapter defines outputs and outcomes and discusses how they can be associated with innovation. Then, it explores and discusses how outputs and outcomes can be linked with sectoral differences, different levels of analysis, and negative outcomes of innovation.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".