Primer

What Is Metascience? The Study and Design of How Science Works

David Hilmer Rex · June 2026 · Updated October 2026 · 10 min read

Summary

Metascience is the study of how science works as a human system, and the deliberate design of better versions of it. It treats the machinery of research (funding, institutions, careers, publishing, incentives) as the object of inquiry, and asks how changing that machinery changes what can be discovered. The field has several strands. Two of the best known aim to make science more reliable and more productive, and another studies the structural problems in research careers, power and funding. All rest on the premise that the social processes of science are contingent and therefore designable. This resource takes a whole-system view, in which these strands belong together. This primer defines metascience, traces its lineage from Bush and Merton through the reproducibility crisis and progress studies, maps its strands, weighs the critiques of the movement, and explains how this resource is organised around it.

A working definition ↑

Michael Nielsen and Kanjun Qiu frame metascience as the search for “new social processes for science.”¹

The field has a descriptive wing and a design wing. The descriptive wing (the quantitative “science of science,” or meta-research, institutionalised when John Ioannidis and Steven Goodman founded the Meta-Research Innovation Center at Stanford in 2014² and mapped at scale by the broader science-of-science research programme³) studies research empirically: its methods, reporting, reproducibility, evaluation, and incentives. The design wing asks the normative question that follows: given how science works, what should we build or change? Metascience, as this resource uses the term, spans both: the study of how science works and the deliberate design of better versions of it.

Metascience as a systemic capability ↑

The definition above casts metascience as something researchers do to science: study its machinery, design better versions. There is a second register, increasingly useful, in which metascience names something the system itself has: the capacity of a research and innovation ecosystem to reform how it funds, organises, evaluates, and shares research, as deliberately as it revises its theories.⁴ On this reading the word points to the system’s own ability to redesign itself, alongside any redesign from outside.

This capability is distributed. It can live at any node (a lab that changes how it credits contributions, a funder that pilots a new grant mechanism, a university that rethinks how it evaluates careers), and a system has strong metascience when the capacity is present across many nodes. Because the parts of a research system are coupled, rigidity in one part is felt by the rest: a funder free to experiment is limited by institutions that cannot, and a promising new organisation starves if the surrounding system cannot resource or absorb it.

Specialised institutions (agency metascience units, meta-research centres, science-of-science labs, science-of-science funders) are organs of this capacity, one part of it among many. They concentrate the work that benefits from depth, scale, or coordination: rigorous meta-research, large controlled trials of funding mechanisms, shared data and infrastructure. At their best they raise the metascientific capacity of every other node. The failure mode is inversion: when a single office becomes the only site of the capability, the system gains a point of specialisation but also a single point of fragility and capture.

This framing speaks directly to a tension noted below: that the movement is concentrated among a small, largely US and philanthropically funded group. Read as a field, that concentration is a limitation to be managed; read as a capability, it is the problem to be solved: the aim is a capacity distributed across the whole ecosystem. The capability register is a normative and design claim, complementary to the study-and-design definition: it names what the field’s design wing is ultimately trying to cultivate.

Understood this way, metascience is a whole-of-system ambition: that a research and innovation system, at every scale, retains the capacity to change and adapt. It is a regulative ideal, never fully reached, and not meant to be, since the arrangements worth having keep shifting as tools, questions, and conditions change. The aim is that nothing is structurally unable to change when it should; a healthy system holds both the capacity to reform itself and the judgment of when to conserve. That capacity (distributed, and never finished) is what the word finally names.

A short lineage ↑

Metascience is older than its current name. Vannevar Bush’s Science, The Endless Frontier (1945) was a work of institutional design: it argued for sustained federal funding of basic research and led to the creation of the US National Science Foundation, building much of the very system metascience now interrogates.⁵ Robert Merton then treated science as a social institution with its own norms (communalism, universalism, disinterestedness, organised scepticism), making the culture of research a legitimate object of study.⁶ And Derek de Solla Price, in Little Science, Big Science (1963), helped found the quantitative study of science later called scientometrics.⁷

The modern revival has two triggers, two decades apart. In 2005 John Ioannidis published “Why Most Published Research Findings Are False,” which crystallised a methodological reckoning that became the reproducibility crisis.⁸ Then, around 2018, a separate conversation about scientific stagnation and institutional reform gathered force. These two impulses, reliability and ambition, are two of the field’s main strands.

Two strands: rigour and ambition ↑

The first strand wants to make science more reliable. It grew out of the reproducibility crisis and runs through open science, preregistration, and registered reports. Its institutional home is Brian Nosek’s Center for Open Science (founded 2013) and projects like the Reproducibility Project: Psychology, which found that only about a third of one hundred published findings reached significance when independently repeated.⁹ Its question is: how do we stop believing things that are not true?

Why do reliability problems persist despite decades of awareness? The clearest structural account comes from Smaldino and McElreath: poor methods persist chiefly through selection pressure, with ignorance and dishonesty playing a smaller part. Labs that prioritise publication output over methodological rigour produce more papers, attract more students, and propagate their practices through a Darwinian dynamic that selects for yield over accuracy. Reviews of statistical power in the social sciences found a mean of around 24%, and that figure did not improve over six decades of calls for reform.¹⁰ The implication is that the reliability agenda cannot be satisfied by education or better guidelines alone; it requires changing the incentive landscape that drives the selection.

The second strand wants to make science more productive and ambitious. It grew out of the stagnation debate and runs through progress studies and the new-institutions movement: ARPA-model agencies, Focused Research Organisations, new funding mechanisms. Its question is: how do we discover more, faster, and attempt the things the current system cannot?

These can pull against each other: rigour can counsel caution and slower publication; ambition can counsel more risk and more failure. The most interesting metascience sits where they meet: how to make science simultaneously more trustworthy and more daring. This resource takes a whole-system view: reliability, ambition, careers, equity and power are parts of one system, and experiments, new institutions and reform of established ones are complementary.

The stagnation question ↑

The empirical backbone of the ambition strand is the finding that research is getting less efficient. Bloom, Jones, Van Reenen and Webb, in “Are Ideas Getting Harder to Find?” (2020), show that research effort has risen sharply while research productivity has fallen across sectors: sustaining the historical pace of Moore’s Law now takes more than eighteen times the researchers it required in the early 1970s, with parallel declines in agriculture and pharmaceuticals.¹¹

A second, independent line of evidence is the apparent decline in disruptiveness. Analysing 45 million papers and 3.9 million patents, Park, Leahey and Funk reported in 2023 that research has grown steadily less likely to break with the past and push a field in a new direction: the average disruption score fell by more than 90% between 1945 and 2010.¹² That finding is itself contested (later analyses argue the measured decline is partly an artefact of citation inflation rather than a real loss of originality), which is instructive in its own right: even the core empirical claims of the stagnation thesis are still live.

Patrick Collison turned this body of work into a public agenda with two essays in The Atlantic: “Science Is Getting Less Bang for Its Buck” (2018, with Michael Nielsen) and “We Need a New Science of Progress” (2019, with Tyler Cowen). The second named progress studies and gave the institutional-reform conversation its broadest intellectual home.¹³ Whether science is genuinely slowing remains disputed; but the perception is itself one of the forces driving the field.

What metascience covers ↑

The field spans several layers: how research is funded and incentivised; how institutions, careers, and talent pipelines are structured; how results are published, validated, and communicated; how decentralized and AI-driven models are changing what is possible; and how the broader political and economic environment shapes what research gets done.

There is no single canonical taxonomy. The most widely used structural map, from Ioannidis and colleagues, organises meta-research around five domains: methods (how research is conducted), reporting (how findings are communicated), reproducibility (whether findings replicate), evaluation (how research is assessed), and incentives (what behaviours are rewarded). Peterson and Panofsky, reading the movement sociologically, identify three converging streams: the quantitative science of science, the open-science reform agenda, and the reproducibility movement. Watney and Gustetic add further resolution, mapping six camps: research integrity, open science, metrics of science, innovation economics, R&D management and implementation, and institutional entrepreneurs.¹⁴ The common thread: “metascience” names a coalition of overlapping projects. None of these maps gives a strand of its own to the structural problems of research careers and culture, such as precarity, the career cost of parenthood, unequal power and the dominance of project funding. This resource treats them as central.

Where the field is now (2026) ↑

Metascience now has homes inside government as well as around it. The UK Metascience Unit, set up in 2023 at DSIT and UKRI, runs trials on live funding: its distributed peer review pilots cut per-application assessment time by up to 65%, and its 2025–26 Novelty Indicators Challenge tested tools for judging how new a piece of research is.¹⁵ The Research on Research Institute (RoRI) works across metascience research, policy and practice; its July 2026 capstone report concluded that current peer review is unsustainable under hyper-competition supercharged by AI.¹⁶ In the United States, the OSTP report Science: A New Golden Age (July 2026) called for metascience units in the science agencies, with agency proposals due on 19 October 2026.¹⁷ In August, NSF framed many of its $1.5 billion in new foundational-research calls as structured experiments it will assess and scale.¹⁸ In Europe, Sam Bogerd has made the case for an experimental metascience unit inside the European Commission, a recommendation of the Heitor Report.¹⁹

Critiques and limits ↑

The movement has its critics, and taking them seriously is part of doing metascience well. A first objection is that science is not one thing: reforms that suit experimental psychology may not fit mathematics, field ecology, or history, and a search for universal “best practices” can flatten a productive disunity. A second is that the movement has leaned heavily on quantitative metrics and on the reproducibility-crisis reform agenda (preregistration, replication) while underusing the qualitative scholarship in science and technology studies that has examined how science works for half a century.²⁰

The sharpest sociological account, from David Peterson and Aaron Panofsky, frames metascience as a “scientific social movement” (a coalition of methodologists, data scientists, and open-science activists) and presses two further points. First, the reproducibility “crisis” that licenses the movement is itself partly an actors’ construction, and some of its foundational evidence (an influential 2016 Nature survey, the Amgen cancer-replication study) has been criticised as methodologically thin. Second, by claiming authority over “science” as a whole, metascience presumes a unity the disciplines may not have and, dressed in a lab coat and speaking in data, mounts a more direct challenge to disciplinary autonomy than science studies ever did.²¹

Three further worries recur. That prescriptions like preregistration can harden into dogma and themselves narrow what research is possible, a diversity cost of exactly the kind the institutional-diversity primer warns against. That the progress-studies framing justifies science largely instrumentally, as the fuel of material progress, when that is neither its only value nor an uncontested one. And that the movement is concentrated among a small, well-connected, largely US and philanthropically funded group, which raises the same question the new-institutions conversation faces: whose priorities set the agenda? State metascience units in the UK and US, and the one proposed for the European Commission, now answer part of that critique (see above).

Finally there is the reflexive test. A field that studies how to improve science is obliged to hold its own prescriptions to the evidence it demands of others. That test is increasingly being run (randomised studies of funding mechanisms, empirical work on the reliability of peer review, the large reproducibility projects), but the honest status is that metascience is young: many of its proposed reforms are plausible and few are yet proven at scale. Its credibility will rest on meeting its own standard.

How this resource is organised ↑

This primer is the front door. Companion primers cover funding mechanisms, people-not-projects funding, fast grants, institutional diversity, enterprise foundations, focused research organizations, decentralized science, and, among the trackers, the ecosystem forces map. They are held together by one question, extending Nielsen and Qiu’s phrasing: what new social processes would let science do more, better and more fairly? Treating the machinery of research as one designable system, with its careers, power and funding mix, is what makes lasting improvement possible.

Cite as: Rex, David Hilmer (2026). What Is Metascience? The Study and Design of How Science Works. https://davidhilmerrex.nu/metascience/what-is-metascience.html