Evaluating the maturity of supporting NTBF policy: evolutionary analysis of two key laws in Iran
Bibliographic record
Abstract
Abstract Policy evaluation has spread rapidly around the world in the last few decades. This study develops and applies a policy maturity model to evaluate the evolution of two significant innovation policies in Iran: the Law on Supporting Knowledge-Based Institutions and Companies (2010) and the Knowledge-Based Production Leap Act (2022). The study focuses on new technology–based firms (NTBFs), known as ‘knowledge-based firms’, which play a crucial role in fostering innovation and economic development. Employing a qualitative methodology, this research integrates a systematic literature review with in-depth interviews conducted with 15 national innovation policy experts. The proposed model identifies four distinct levels of policy maturity, ranging from Undefined to Broad Perspective. Findings reveal that the 2010 law aligns with a Narrow Perspective, characterized by government-centric interventions. In contrast, the 2022 act reflects an Intermediate Perspective, reflecting increased cooperation between public and private sectors. To progress towards the Broad Perspective—where civil society plays an active role—this study recommends strengthening macrolevel governance, institutionalizing transparent evaluation and learning mechanisms, promoting stakeholder engagement, and enhancing the resilience of innovation policies. The research contributes theoretically by offering a structured framework for evaluating innovation policies in developing countries, addressing the need for context-specific assessment tools beyond existing models.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".