The diverse niches of megajournals: Specialism within generalism
Bibliographic record
Abstract
Abstract Over the past decade, megajournals have expanded in popularity and established a legitimate niche in academic publishing. Leveraging advantages of digital publishing, megajournals are characterized by large publication volume, broad interdisciplinary scope, and peer‐review filters that select primarily for scientific soundness as opposed to novelty or originality. These publishing innovations are complementary and competitive vis‐à‐vis traditional journals. We analyze how megajournals ( PLOS One , Scientific Reports ) are represented in different fields relative to prominent generalist journals ( Nature , PNAS , Science ) and “quasi‐megajournals” ( Nature Communications , PeerJ ). Our results show that both megajournals and prominent traditional journals have distinctive niches, despite the similar interdisciplinary scopes of such journals. These niches—defined by publishing volume and disciplinary diversity—are dynamic and varied over the relatively brief histories of the analyzed megajournals. Although the life sciences are the predominant contributor to megajournals, there is variation in the disciplinary composition of different megajournals. The growth trajectories and disciplinary composition of generalist journals—including megajournals—reflect changing knowledge dissemination and reward structures in science.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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, 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".