National Systems of Innovation and Market and Government Failure
Bibliographic record
Abstract
Innovations do not occur in isolation. There is a system or framework in which different actors are connected to and affect each other. This chapter discusses three interrelated concepts: market failure, government failure, and the National Innovation System (NIS) and the government’s role in innovation. After defining and providing examples of market and government failure, this chapter explores how market and government failures impact innovation as well as the NIS and the government’s role in innovation. Understanding government operations, interpreting the relationship between public and private organizations, and evaluating government innovations are complex tasks. Each policy is subject to limitations and unexpected consequences. In many cases, the market fails. To correct these market failures, the government intervenes, changes, or implements a new policy or uses a tool (e.g., subsidy or tax). However, the government’s involvement in the market may lead to government failure. Therefore, fixing market and government failures is not easy, although innovations can help to fix both failures. In addition, this chapter discusses how innovative activity affects economic growth, employment, and entrepreneurship, as well as how technological innovations can enhance social welfare and living standards.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".