Strategies to Increase the Number of Open Access Journals: The Cases of Elsevier and Springer Nature
Bibliographic record
Abstract
In recent years, major commercial publishers have strengthened their presence in both the subscription journal market and the open access journal market. Examining 447 journals from Elsevier and 550 from Springer Nature, this study investigates three strategies for enlarging the number of gold open access journals: the launch of new journals, mergers with other publishers, and partnerships with research institutes. The results reveal that these publishers adopted different strategies for expanding their journal portfolios. While Springer Nature relied significantly on merging with established publishers, Elsevier recently launched many new journals independently. Approximately 60 per cent of Springer Nature journals and 45 per cent of Elsevier journals are published on behalf of research institutes. Therefore, collaboration with research institutes has contributed to the increasing number of journal titles. As major publishers expand their open access businesses, it is necessary to monitor their activities from a policy perspective of pro-competition.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Scholarly communicationOpen science Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Scholarly communicationOpen science Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.029 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.025 | 0.019 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".