Scientific Autonomy in the Structural Bubble

Science often appears, from the outside, to be a free arena in which the best ideas prevail through evidence, logic, and sound methods alone. In reality, however, research is almost always embedded in fixed structures. It depends on funding, institutional backing, publications, visibility, and public or political relevance. As a result, conditions emerge in which some questions are easier to ask, some methods are more likely to be rewarded, and some findings spread far more quickly than others. This is exactly what this text describes as a structural bubble: not a secret plan, not a grand conspiracy, but a system in which selection mechanisms continuously help determine what becomes prominent in science and what receives little attention.

This structural bubble consists of several layers that work together. One layer is funding. Whoever finances research often also sets priorities, expectations, and boundaries. A second layer is the publication system. Peer review, journal rank, citation counts, and institutional prestige do not function only as neutral signs of quality. They also often act as filters that determine what becomes visible and what is taken seriously at all. A third layer consists of consensus platforms and secondary knowledge structures, meaning systems that collect, organize, and pre-structure knowledge for others. These systems often reinforce precisely those views that are already dominant.

The fourth layer then becomes especially important: artificial intelligence. In this context, AI is not simply a practical tool that makes it easier to summarize texts or find literature more quickly. It becomes part of the very infrastructure through which scientific visibility is produced. If such systems are built primarily on what is already frequently cited, prestigious, and institutionally established, then they reinforce those same patterns once again. What is already well known becomes even more visible. What is already accepted appears even more self-evident. And what stands at the margins disappears more easily from view. This creates a second filter: not only does the scientific system itself sort and prioritize, but so do the technical systems that later tell us what is relevant, credible, and authoritative.

This also changes the question of what scientific freedom actually means today. It is no longer enough for research to be permitted in theory. What matters is whether different questions still have a real chance to be pursued, published, and found. Freedom in science therefore means, first, that the space of possible questions remains open. Second, it means that authority arises from evidence and method rather than from prestige alone. And third, it means that the pathways through which knowledge is discovered, sorted, and summarized must not become so narrow that the same perspectives are automatically favored again and again.

The decisive point, then, lies not only in the content itself, but in the structures through which content becomes visible. Science is shaped not only by facts, but also by funding logics, evaluation systems, publication routines, platforms, and algorithmic selection. The more deeply artificial intelligence is built into these processes, the more important the question becomes whether it supports genuine diversity of thought or simply strengthens what is already powerful. This analysis begins precisely there: with the insight that knowledge is never simply found, but is always also filtered, weighted, and distributed.

The full scientific article can be found in the Journal Frontiers in Research Metrics and Analytics:

Elias Rubenstein (2026): Scientific autonomy in the structural bubble: from institutional bias to AI-mediated consensus
https://doi.org/10.3389/frma.2026.1766504