{"id":5986,"date":"2026-07-24T10:00:14","date_gmt":"2026-07-24T13:00:14","guid":{"rendered":"https:\/\/blog.scielo.org\/en\/?p=5986"},"modified":"2026-07-24T10:38:37","modified_gmt":"2026-07-24T13:38:37","slug":"the-future-of-the-scientific-article-in-the-age-of-ai","status":"publish","type":"post","link":"https:\/\/blog.scielo.org\/en\/2026\/07\/24\/the-future-of-the-scientific-article-in-the-age-of-ai\/","title":{"rendered":"The Future of the Scientific Article in the Age of AI"},"content":{"rendered":"

By Ernesto Spinak<\/strong><\/p>\n

Introduction<\/h3>\n
\"Black-and-white<\/a>

Image: Nathan Dumlao via Unsplash<\/a><\/p><\/div>\n

The traditional model of the scientific article, conceived centuries ago to accelerate the exchange of results, appears to have come to an end. We are currently in its survival phase, in which the validity of the immutable PDF document as a \u201cblack box\u201d (which some consider a \u201cfossil\u201d) is being questioned, since its rigidity facilitates fraud by allowing manipulated data or inserted metadata to be concealed.<\/p>\n

A possible transition points toward science as a continuous and constantly evolving process, where knowledge is not condensed into a single product but rather into open research notebooks (such as Jupyter Noteboks<\/a>). Here, code and data coexist in an auditable manner, ensuring transparency by documenting who made a decision, when it was made, and what information was used.<\/p>\n

Current status: integrity, ghost citations, hidden references<\/h3>\n

One of the threats is the manipulation of metadata through so-called \u201csneaked references\u201d. These are citations that appear neither in the body of the article nor in its visible bibliography.<\/p>\n

The first study on the topic, published on arXiv last year, Detection of metadata manipulations: Finding sneaked references in the scholarly literature<\/a> 1<\/sup><\/a>,demonstrates that the global scientific communication infrastructure has a vulnerability whereby \u201cit is possible to artificially manipulate scientific impact by inserting nonexistent citations into an article\u2019s metadata without altering the published document, thereby compromising the reliability of international citation-based evaluation systems. The results are extraordinarily revealing. An analysis of 4,077 documents identified more than 80,000 artificially inserted fraudulent citations, distributed across 2,787 manipulated articles<\/em>.\u201d<\/p>\n

The main purpose of these phantom citations is to artificially inflate citation-based performance indicators, such as the h-index, journal Impact Factors, and Field-Weighted Citation Impact (FWCI). Since these metrics form the basis of prestigious global rankings (such as the Shanghai Ranking or Clarivate\u2019s lists of highly cited researchers), fraud ultimately undermines the reliability of the entire academic evaluation system.<\/p>\n

Ghost citations are inserted directly into the metadata of articles registered with agencies such as Crossref, causing them to spread across major scientometrics platforms. As a result, these frauds have forced infrastructure institutions to take drastic measures, such as permanently revoking Crossref membership and, more importantly, triggering a \u201cconfidence crisis\u201d, s pointed out in an editorial published by ACS Nano<\/em> in early 2026, Peer Review and AI: Your (Human) Opinion Is What Matters<\/a> 2<\/sup><\/a>,which details how AI-generated evaluations are homogenizing science, destroying critical thinking, and creating \u201cfalse efficiencies\u201d that overwhelm human editors).<\/p>\n

This phenomenon exposes an \u201contological crisis\u201d in science, in which the \u201cpublish or perish\u201d incentive system leads publishers to prioritize volume and impact at the expense of the soundness of the discovery. (See the editorial entitled More Versus Better: Artificial Intelligence, Incentives, and the Emerging Crisis in Peer Review<\/a> 3<\/sup><\/a>, which analyzes the behavior of scientific journals following the mass adoption of LLMs and concludes that AI, combined with traditional incentives from funding agencies and transformative agreements, is driving academia to produce more volume rather than better research, thereby eroding standards of readability and conceptual depth.)<\/p>\n

Consequently, the saturation of the system through the use of AI to generate fraudulent articles and citations threatens to create a \u201cclosed loop\u201d in which some computers write and others review, stripping the scientific validation ecosystem of empirical rigor and human intervention. This makes it difficult for human researchers to distinguish between legitimate science and \u201csophisticated junk\u201d based on fabricated data.<\/p>\n

\u00a0What if we moved toward the complete automation of science?<\/h3>\n

The risk of full automation in science is not merely a technological shift but is described in the sources as a response to the ontological crisis affecting the very essence of knowledge and its validation. The main risks mentioned are as follows:<\/p>\n