Mapping Knowledge Structure of Science, Technology and Innovation Policy Making Studies: A Co-Occurrence Analysis
Bibliographic record
Abstract
Purpose: This study aimed to map and analyze the knowledge structure of science, technology, and innovation policymaking studies using a co-occurrence network. Understanding the trends in the literature related to science, technology, and innovation policymaking enables policymakers to gain a broader perspective on significant global issues. Additionally, researchers can gain insights into the most prevalent topics in this field and navigate the boundaries of knowledge in science, technology, and innovation policymaking.Methodology: The current research is a descriptive study that employs scientometric indicators along with documentary and social network analysis methods. The research population consists of 3,868 articles and reviews published between 1969 and 2023 in the field of science, technology, and innovation policymaking. Data for the study was collected on June 3, 2023, by accessing the Web of Science database (a product of Clarivate Analytics). Microsoft Excel was utilized to analyze the research data and create a geographical distribution map. Additionally, Vosviewer software was employed to visualize the co-occurrence network of papers in the field of science, technology, and innovation policymaking, while Gephi was used to clean and remove unrelated keywords.Findings: The number of published articles on the topic of science, technology, and innovation policymaking has been steadily increasing. Among the countries, the United States, England, China, the Netherlands, and Germany have produced the highest number of publications in this field. An analysis of journal publications revealed that the Sustainability Journal, with 89 articles; the Environmental Science and Policy Journal, with 74 articles; and the Journal of Cleaner Production, with 69 articles, have the most publications related to science, technology, and innovation policymaking. The co-occurrence network in this field consists of 221 nodes and 8035 unique links, and the most frequently occurring keywords among these nodes include science, policy, policy making, innovation, government, and technology. Furthermore, over the past five years, topics such as COVID-19, big data, sustainable development, data science, citizen science, circular economy, urbanization, energy transition, and CO2 emissions have been extensively discussed. The co-occurrence network in the field of science, technology, and innovation policymaking consists of five clusters: sustainable development, public health, climate change, government support, and public participation. These clusters represent different aspects of science, technology, and innovation policymaking.The findings indicate that the articles published by Iranian researchers on science, technology, and innovation policymaking have received 924 citations. In other words, each of them received an average of 7.7 citations, and 77.59 percent of them were cited at least once. In addition, these articles have performed less than the global expectation by obtaining a category-normalized citation impact equal to 0.92. Also, 79.38 percent of these scientific productions result from international collaborations and the highest amount of collaboration has been done with researchers from the USA, England, Australia, and Canada.Conclusion: The policymaking process for science and technology is highly complex, and influenced by various factors, including global conditions, challenges, developments, trends, country of origin, and territorial considerations. To promote sustainable development goals, science and technology policies should prioritize innovation and entrepreneurship. Additionally, governments should focus on supporting activities that contribute to economic prosperity in this area. Health is a vital economic sector that not only directly impacts economic growth but also influences the development of other sectors. Policymakers in science and technology across various countries emphasize the significance of health research and the rapid deployment of new technologies, such as pharmaceuticals, diagnostic tools, remote treatments, and medical equipment. Science, technology, and innovation are crucial for sustainable development, as they aid in the formulation and implementation of policies and regulations concerning climate change. These policies may involve establishing standards, implementing energy regulations and taxes, utilizing tools to control pollution and greenhouse gas emissions, and protecting water resources and the environment. The government plays a crucial role in the formulation of science and technology policies, especially in developing countries, where it aligns with national priorities through various policy instruments. These instruments, often referred to as governance tools, are measures utilized by the government to accomplish specific policy objectives. Public participation in the science and technology policy-making process allows society to directly influence policies and decisions in this domain. This promotes transparency, trust, and the empowerment of society in matters related to science and technology, ultimately resulting in the adoption of improved policies, informed decisions, and positive outcomes for the community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.028 | 0.063 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".