Economic Growth and Internet Usage Impact on Publication Productivity among ASEAN’s and World’s Best Universities
Bibliographic record
Abstract
Measuring the number of papers which are published each year, publication productivity is the factor which shows the reputation of universities and countries. However, the effect of growing economy and using internet on the publication productivity in Asian countries has not been discovered yet. The present research is going to figure out the publication productivity among the elite universities in Asian countries and also ten top universities around the world in the last twenty years (from 1993 to 2012). Furthermore, the current research is aimed to study the relationship among publication, gross domestic product (GDP) and internet usage. It is worth to mention that the publication of the top Ten Malaysian Universities was regarded for the similar period of time. To get the exact numbers of documents like papers, conference articles, review papers and letters which are published by the universities in the last twenty years, the writer of the same paper used the Science Direct database. Moreover, the data for GDP and the number of internet usage was collected through the World Bank database (World Data Bank).To compare all kinds of publications,one-way ANOVA was used and to investigate the impact of economic growth and internet usage on publication productivity, multiple regression analysis was applied.The results showed that the rate of publication growth was 1.9, 20.9, and 65.5 % in top universities in the world, ASEAN countries and Malaysia, respectively.The results also showed that there was a positive and significant correlation between GDP and the number of internet users with the number of publications in ASEAN and Malaysian universities. Internet usage had much more influence in comparison with the GDP in predicting the number of publications among these groups except for top ten Malaysian universities from 2003 to 2012. In summary, publication trends in top ten Malaysian and ASEAN universities are promising. However, policy makers and science managers should spend much more percentage of their GDP on Internet facilities and research studies that their outputs lead to more rapid economic growth and internet usage.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".