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Record W6925999431 · doi:10.22070/rsci.2023.17457.1654

Analysis of the State of Cooperation Between University and Industry from the Aspect of Financial Support

2024· article· en· W6925999431 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtist diversity and phylogeny
Canadian institutionsnot available
Fundersnot available
KeywordsState (computer science)Data collectionDescriptive statisticsCensusPopulationDescriptive researchKey (lock)Statistical analysis

Abstract

fetched live from OpenAlex

Purpose: Given that university-industry cooperation is associated with the transfer of knowledge and technology, one of the key indicators of a country's development is the effectiveness of collaboration between universities and industry. Due to the significance of this topic, the present article seeks to analyze the state of cooperation between Iranian universities and industry based on the co-publication of articles indexed in the Web of Science database from 2010 to 2022, particularly in terms of financial support. The findings of this research will inform national policymakers and planners about the current state of university-industry cooperation in Iran, enabling them to devise appropriate strategies to enhance this collaboration.Methodology: The research method employed in this study is both descriptive and quantitative, utilizing scientific techniques such as word co-occurrence analysis. The statistical population for this research comprises 2,891 articles. This study encompasses all articles that received financial support, focusing on the collaboration between universities and industry in Iran from 2010 to 2022; therefore, no sampling method was applied. Instead, a census sampling method was utilized. To examine the collaboration between universities and industry through scientific publications (articles), each article must include at least one author affiliated with an industrial organization and one author affiliated with an academic institution, both of whom must have a financial sponsor. Specific labels were used to identify the organizations involved. Data collection was conducted using the Web of Science database, and the data were analyzed and visualized using BibExcel and VOSviewer software.Findings: According to the findings of the current research, the highest number of financial supports for cooperation between industries and universities in Iran was recorded in 2021, with 430 articles published. In contrast, the lowest number was recorded in 2010, with only 89 articles. On the international front, 78 countries have co-published with Iran, with the United States leading in the number of collaborative articles. Other countries that have engaged in significant cooperation with Iran include England, Canada, Australia, France, New Zealand, China, Germany, Italy, and Russia are other countries that have had a large number of cooperation cases in Iran. The data related to financial support institutions showed that the National Iranian Oil Company along its subsidiaries (9.79%); Iran National Petrochemical Company along its subsidiaries (4.74%); Support fund for researchers and technologists (4.08%); Islamic Azad University (3.22%); National Gas Company and its subsidiaries (3.18%); University of Tehran with (2.94%); and Tehran University of Medical Sciences (2.80%) have provided the most financial support. Out of a total of 251 subject areas of Web of Science, 151 areas have received financial support in cooperation between the university and the industry. Based on thematic analysis, chemical engineering fields (10.27%); environmental sciences and materials science (4.28%); energy and fuels (4.21%); and water resources (3.65%), are the most used topics in the articles.Conclusion: The annual growth rate of articles receiving financial support indicates that the 13-year collaboration between universities and industry has experienced significant fluctuations, with some years witnessing a decline. Furthermore, the leading industries providing financial sponsorship include oil, petrochemicals, and gas. Most of these companies are supported by government organizations and are among the most profitable in the country. Consequently, a substantial portion of the investments in this collaboration is derived from the government budget. In terms of the subjects covered in the articles, the results reveal that, while the fields are diverse and extensive, there is a noticeable absence of certain topics, particularly in the humanities and social sciences.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.049
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0150.022
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.107
GPT teacher head0.444
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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