Scientometric Analysis of Articles on the Consumption of Cultural Goods
Bibliographic record
Abstract
The purpose of this research is to conduct a scientometric study of articles on the consumption of cultural goods. In order to achieve the research objectives, the research questions are as follows: What is the trend in publication and the rate of global and local citations based on the year and types of sources publishing articles in the Clarivate Analytics citation database? What are the predominant topics, subject areas, countries, organizations, languages, authors, and journals publishing articles on the consumption of cultural goods? What are the rates of citation, co-citation, and co-authorship among countries, organizations, and languages publishing these articles? Finally, what does the co-occurrence map of keywords reveal about articles on the consumption of cultural goods in the Clarivate Analytics citation database? This study is applied research that employs descriptive-analytical and scientometric methodologies. The statistical population for this research includes 556 articles on the consumption of cultural goods indexed in the Clarivate Analytics Web of Science database, covering the years 1986 to 2018. The data analysis tools used are HisCite, VOSviewer, and Excel software. The findings indicate that the publication trend of articles has fluctuated slightly over the years, with most citations related to older articles. The subjects of these articles primarily fall within the fields of business, economics, sociology, anthropology, cultural studies, ecology, social sciences, and life sciences. Additionally, the findings reveal that the United States, England, and Canada produced the most articles and received the highest number of local and global citations. Among universities, the University of London, the University of Guelph, Macquarie University, Lancaster University, and University of Surrey had the highest local and global citations. In terms of journals, the Journal of Consumer Research and the Journal of Cultural Economics received the most local and global citations, with most articles published in high-impact journals (Q1). The findings show that overall citation collaboration among authors, countries, and organizations is low, and the network of keywords is quite dispersed. The results also indicate that local citations to articles and scientific collaboration among authors are significantly lower than their global citations. Therefore, cultural policymakers should strengthen academic cooperation at both local and international levels and create incentives to enhance scientific collaboration among authors, countries, and organizations.
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 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 | 0.016 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.194 | 0.223 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".