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Enregistrement W6963828932 · doi:10.22070/rsci.2024.18091.1686

Mapping Knowledge Structure of Science, Technology and Innovation Policy Making Studies: A Co-Occurrence Analysis

2024· article· en· W6963828932 sur OpenAlexaboutno aff

Notice bibliographique

RevueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueUniversity-Industry-Government Innovation Models
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSocial network analysisSustainabilityField (mathematics)Product (mathematics)BibliometricsInnovation managementBig dataProduct innovationPopulation

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesBibliométrie, Communication savante
Catégories consensuellesBibliométrie
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,112
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0280,063
Études des sciences et des technologies0,0000,001
Communication savante0,0010,006
Science ouverte0,0020,002
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,211
Tête enseignante GPT0,517
Écart entre enseignants0,306 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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