Developing a maturity model to evaluate innovation policy across generations
Bibliographic record
Abstract
Purpose This paper aims to develop a maturity model for innovation policy programs that assesses policy programs according to the literature. Reviewing the historical emergence and decline of themes in innovation policy literature with a combination of bibliometric and thematic analysis methods resulted in identifying 36 main themes. Design/methodology/approach Accordingly, the maturity model is designed with four levels and nine axes. The model’s axes are market creation, innovation value, innovation impact, actors’ interactions, actors’ capabilities, social participation, reflective learning, coordination in implementation and resource allocation. At Level 0 (undefined), the policy does not contain any mechanism to support different aspects of innovation development, while the first level focuses on public organizations. Levels 2 and 3 add private firms and civil societies to the policymaking process, respectively, ensuring the development of an inclusive innovation policy program. Findings Finally, the maturity model has been used to evaluate Iranian innovation policymaking progress, focusing on the “Support of Knowledge-based Firms and Institutions and Commercialization of Innovations and Inventions Act” (2010) and the “Knowledge-based Production Boost Act” (2022) as its central policy documents. Originality/value Different generations have described the innovation development process, and theoretical concepts embedded in innovation policies are divided into different generations. However, no model has evaluated the innovation policy programs to check their evolution according to the growing understanding of innovation policy generations.
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".