Citation Impact of Open-Access Publications in the UAE Research Landscape
Bibliographic record
Abstract
This study analyzes Open Access (OA) publication patterns and their citation impacts in the UAE from 2016 to 2021 using the SCOPUS database. Addressing a critical research gap, it focuses on the UAE's unique academic and cultural context to offer insights into how OA practices influence citation metrics. By examining institutional factors and the performance of hybrid, green, and gold OA models, the study highlights the need for a national OA policy. The findings show that from 2016 to 2021, the UAE experienced a significant shift towards OA publishing, with OA publications increasing from 27% to 39%, and a 296% growth in OA publications compared to a 56% increase in paywalled articles. Despite the UAE's OA share being below the global average, several institutions outperformed this benchmark. OA articles generally received more citations than paywalled ones, with hybrid OA being the most cited category over green and gold OA.
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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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.041 | 0.061 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| 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".