Relationship among Economic Growth, Internet Usage and Publication Productivity: Comparison among ASEAN (Association of Southeast Asian Nations) and World’s Best Countries
Bibliographic record
Abstract
Publication productivity, as measured by the number of papers, has been regarded as one of the main indicators of reputation of countries and institutions. Nevertheless, the relationship among research publications, economic growth and World Wide Web in ASEAN countries is still unclear. The main intention of this study was to identify publication productivity among ASEAN and the world’s top ten countries in the last 16 years (1996-2011). This study also aimed at finding the relationship among publication, gross domestic product (GDP) and internet usage. Furthermore, the publication trend in the 10 first Malaysian universities was evaluated for the same periods. Scopus database was used to find the overall documents, overall citations, citations per document and international collaboration from 1996 to 2011 for each country. The World Bank database (World Data Bank) was used to collect the data for GDP and the number of internet users. Moreover, to evaluate 10 top Malaysian universities, the number of published articles, conferences, reviews, and letters for the same periods was collected. The results of this study showed significant differences among ASEAN and top 10 countries regarding publication productivity. Moreover, a positive and significant relationship was observed between indices, GDP and internet usage for these countries. Surprisingly, international collaboration had a significant and negative relationship with economic growth. Malaysia had fewer citations per document (7.64) and international collaboration (36.9%) among ASEAN countries. In conclusion, international collaboration between academic institutes and researchers is influenced by economic growth and access to internet in the countries. Furthermore, publication trends in ASEAN countries are promising. However, policy makers and science managers should try to find different ways to increase the quality of the research publication and to raise citation per document.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".