The diverse niches of megajournals: Specialism within generalism is a research paper published in Journal of the Association for Information Science and Technology (2019). On theSindex it has a DataRank of 0.457. It has been cited 20 times.
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.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.457
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.