Research performance of the GCC countries: A comparative analysis of quantity and quality
Bibliographic record
Abstract
Given the increased focus on scientific research in the Gulf Cooperation Council (GCC) countries, it is important to have a thorough bibliometric study about their research productivity and its progress over a long period of time. Using the world's largest bibliometric database (Scival/Scopus), we analyzed the research output of the GCC countries, from 1996 to 2020, in various disciplines. We considered raw metrics of quantity (number of articles) and quality (citations, citations/article, and Field-Weighted Citation Impact -FWCI), and then normalized them to population size, Gross Domestic Product (GDP), Gross Expenditure on Research and Development (GERD), and number of researchers. Over the past 25 years, the GCC countries have witnessed an increase in research productivity, with Saudi Arabia having the highest research output ( ca. 38,000 articles for 2020) and Qatar having the highest fold growth (77.6-fold increase). The GCC countries had diverse research portfolios with varying growth over the years across almost all disciplines. When normalized to population size or GDP, growth rates were dampened for all GCC countries. The increased research output in the GCC was coupled with a high percentage of international collaborations and a reasonable increase in the quality of publications. While the research performance in the GCC countries has promisingly enhanced, it remains low compared to that of international countries (Switzerland, Singapore, and Canada) which have remarkable research productivity. Considering the GCC's economic standings and the potential for further growth, the GCC countries would need increased investment in scientific research and in human capital to be able to catch up with the highest international standards in research.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.035 | 0.059 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".