Publication Patterns in Engineering: A Quantitative Comparison of Open Access and Subscription-Based Journals
Bibliographic record
Abstract
This study compares the publication performance of Open-Access (OA) and subscrip-tion-based (SB) journals in Engineering, using bibliometric indicators from Scopus (2023 view). A total of 3012 active Engineering journals were analysed, of which 757 are OA, and 2255 are SB. Four metrics were examined for the period 2020–2023: CiteScore, total citations, number of published documents, and the percentage of cited articles, stratified by CiteScore quartiles (Q1–Q4) and the top 10% CiteScore group. SB journals concentrate most citations and tend to achieve higher mean CiteScores, larger publication volumes, and higher percentages of cited articles in the upper strata, with statistically significant differences on several indicators. At the same time, OA journals display CiteScore medians that are very similar to those of SB journals and lower var-iance for several indicators, particularly within the top 10% group, indicating more consistent performance among well-established OA titles. OA journals represent around one quarter of Engineering journals in Scopus, but remain underrepresented in the most highly cited segment. These findings suggest a hybrid configuration in which SB journals retain dominance at the top of the impact hierarchy. In contrast, OA jour-nals offer competitive and more homogeneous outlets with implications for publication strategies and open-access policies in Engineering.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.052 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 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".