Strategies to Increase the Number of Open Access Journals: The Cases of Elsevier and Springer Nature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.120 | 0.155 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.015 | 0.081 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.216 | 0.094 |
| Open science | 0.019 | 0.012 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it