Bibliographic record
Abstract
With their broad scope, high publishing volume, a peer review process based on the scientific soundness of the content, and an open access model, mega journals have become an important part of scholarly publishing.The main aim of this paper is to determine the most important factor that influenced researchers’ decisions to submit their academic work to these type of journal. To this end, an online survey has been disseminated from November 2016 to August 2017, targeting the corresponding authors of the European Scientific Journal, ESJ. Data from 413 corresponding authors was collected.The focus was mainly on how they discover the journal and what led them to submit a paper to the journal. However, questions concerning their satisfaction with the peer review procedure were also part of the survey.The results have shown that a recommendation of a colleague is not only the main channel through which authors found out about the journal, but is also the major reason they decided to submit their paper to a mega-journal. Furthermore, the quality of the editorial board of the journal, the strong portfolio of papers and the open access concept are also significant factors in encouraging submission to a mega-journal. A majority of the respondents are satisfied with the communication and peer review procedure of the mega-journal, which might encourage new submissions in the future.
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.
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 | 0.023 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".