Disruptive innovation: a new model for public sector services
Bibliographic record
Abstract
Purpose This article aims to provide examples of opportunities to implement disruptive innovation and offer a framework to introduce it in the public sector – proposing a way to use innovation to make public programs radically cheaper without slashing services. Design/methodology/approach By focusing on the public sector job to be done – promoting public safety through incarceration vs electronic monitoring – can illuminate how to accomplish the core goals of an existing process in a different way. Findings The paper finds that the best place to start disruptive innovation tends to be in a market segment that is vastly over‐served or not served at all by the current, dominant model of delivery. Practical implications Government has an array of tools and channels that can be used to foster the growth of disruptive technologies. Originality/value From homeland security to education, from health care to defense, what is needed are innovations that break traditional trade‐offs, particularly that between price and performance. Disruptive innovation offers a proven path to accomplish this goal and in the process transform public services.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".