Why Recognizing and Funding Scientists Is a Common Good

Getting your Trinity Audio player ready...

Recognizing and funding a scientist may appear to benefit one individual. In reality, the consequences can spread throughout society.

A supported scientist gains time, resources, and visibility to prove a theorem, conduct an experiment, maintain scientific software, publish data, verify another researcher’s results, or complete a difficult long-term project. Once communicated, these outputs can be reused by researchers, companies, educators, governments, and future generations.

The scientist receives the support, but society can receive the knowledge.

Recognizing and financing scientists should therefore be understood not merely as assistance to particular people, but as investment in humanity’s shared intellectual infrastructure.

Why Scientific Support Is a Common Good

A grant, salary, prize, or donation is paid to a particular person or organization. The payment itself is not a public good.

The knowledge produced with that support, however, can have public-good characteristics.

A mathematical theorem can be used by many people without being consumed. An openly published dataset can support research questions that its creators never anticipated. A free-software library can be copied, inspected, improved, and used by laboratories throughout the world.

UNESCO’s Recommendation on Open Science describes open science as a global public good and argues that scientific knowledge, infrastructure, and benefits should be made broadly accessible.

Scientific knowledge is not automatically accessible or useful. Research may remain unpublished, hidden behind paywalls, poorly documented, impossible to reproduce, or simply unknown. Nevertheless, when knowledge is openly communicated and reusable, its benefits are not limited to the scientist who produced it.

Funding is individual at the point of payment, but its scientific consequences can become a common good.

How Helping One Scientist Can Help Everyone

Scientific progress is cumulative. New discoveries depend on earlier definitions, measurements, proofs, experiments, software, datasets, reviews, and failed attempts.

A scientist rarely creates an entire technological or intellectual advance alone. Instead, the scientist produces one component that other people can inspect, reuse, combine, and improve.

The relationship between scientific support, reusable knowledge, public benefits, and AIIM is illustrated below.

Figure: The upper sequence shows how recognition and funding can generate knowledge spillovers and broader public benefits. The lower sequence shows the proposed AIIM cycle from donations and contribution evidence to AI-assisted assessment, funding, and further work.

Recognition and funding can increase a scientist’s capacity to work. That work may produce:

• research papers and monographs;
• mathematical proofs;
• experimental results;
• datasets and benchmarks;
• scientific software;
• replication studies;
• reviews and error reports;
• methods, definitions, and technical standards.

Other people can then inspect, criticize, reproduce, teach, combine, or improve these outputs. The ultimate beneficiaries may include people who never knew the original scientist and never contributed directly to the research.

Knowledge Creates Spillovers

Economists use the term knowledge spillover when knowledge created by one person or organization benefits others who did not fully pay for its creation.

A mathematician cannot normally charge every future engineer who applies a theorem. The author of an open-source library cannot demand payment from every later project that depends on it. A scientist who publishes a useful dataset may receive no compensation from most of the researchers who later use it.

This difference between private reward and social value creates a structural funding problem.

Research may be highly valuable to society while producing little direct income for its creator. Markets alone may therefore underfund:

• fundamental research;
• open scientific infrastructure;
• replication;
• maintenance;
• negative results;
• research with long-term or unpredictable applications.

The OECD explains that public support for research and innovation is necessary partly because private incentives may not produce enough investment in research with broad social benefits.

Research on R&D spillovers similarly distinguishes between the returns captured by the original investor and the larger social value created when knowledge spreads to other organizations. See the NBER paper Have R&D Spillovers Changed?.

This does not mean that every research project deserves funding. It means that a scientist’s commercial income is not a reliable measurement of the total value that the scientist may create.

Recognition Is Part of Scientific Infrastructure

Funding provides time and material resources. Recognition provides discoverability.

When a contribution receives justified recognition, other researchers are more likely to examine it, cite it, test it, or build upon it. Recognition can also help a scientist find collaborators, attract resources, and continue working.

This is especially important for work produced outside famous universities or conventional academic networks. Valuable research can be overlooked because its author lacks institutional prestige, professional connections, or the resources needed for promotion.

Recognition performs several productive functions:

• Discovery: researchers learn that a contribution exists.
• Coordination: people working on related problems find one another.
• Verification: significant claims attract scrutiny and replication.
• Preservation: useful work is less likely to disappear.
• Motivation: contributors see that valuable work can receive support.
• Continuation: scientists gain the ability to develop their work further.

Recognition must not be confused with declaring a scientific claim correct. A contribution may deserve examination without deserving acceptance.

A fair system must distinguish between visibility, evaluation, verification, reputation, and funding.

The Scientist Is Not the Only Beneficiary

Suppose a donor supports a scientist who develops an open mathematical method.

The direct recipient is the scientist. But the method might later be used by:

• another mathematician proving a stronger theorem;
• a computer scientist designing an algorithm;
• an engineer optimizing a system;
• a teacher explaining the subject;
• a developer implementing the method;
• a company building a useful product;
• researchers working in a country with limited funding.

The original donor may never know these later users. The scientist may not be able to predict them. Yet the causal chain began when the scientist received sufficient support to complete and communicate the work.

The same principle applies to experimental science.

A dataset produced for one purpose may become valuable for another. A negative result can prevent other laboratories from wasting resources. Maintenance of an old software dependency can preserve the functionality of hundreds of newer projects.

Helping one scientist can strengthen the productive capacity of an entire research network.

Why Existing Funding Systems May Miss This Value

Conventional research funding usually asks scientists to describe future work before it exists.

Applicants must prepare proposals, estimate outcomes, satisfy institutional requirements, and compete for the attention of a limited number of reviewers.

This model can fund excellent research, but it has structural limitations:

• unexpected discoveries are difficult to promise in advance;
• independent researchers may lack institutional eligibility;
• long monographs may not fit standard grant formats;
• maintenance and scientific infrastructure receive limited prestige;
• replications and negative results may be neglected;
• highly unfamiliar work may be difficult for reviewers to assess;
• institutional reputation may influence decisions separately from the contribution itself.

Funding systems can therefore confuse scientific merit with institutional prestige.

As discussed in The Free-Market Defense of Science Donations, voluntary scientific funding remains meaningful even when donors do not receive a direct financial return. Knowledge can create value that its producer cannot fully capture.

A stronger funding ecosystem should evaluate not only promises and affiliations, but also documented contributions that already exist.

Helping Scientists Through AIIM

AI Internet-Meritocracy, or AIIM, is Science DAO’s experimental approach to allocating donated money to scientists and free-software developers according to documented contributions.

The central idea is to examine evidence of completed public work rather than relying exclusively on grant proposals, academic titles, or institutional status.

Relevant evidence may include:

• scientific publications;
• ORCID records;
• research monographs;
• software repositories;
• datasets;
• reviews and replications;
• other documented outputs.

The AIIM user guide explains how researchers and developers can connect information about their contributions.

The proposed funding cycle is:

Donations → contribution evidence → AI-assisted assessment → funding → further work → reassessment

The lower part of the diagram above illustrates this cycle.

Potential Advantages of AIIM

Evaluation After Contribution

AIIM can examine work that has already been produced. This reduces dependence on predictions about discoveries that may or may not occur.

Support for Independent Researchers

A contribution can potentially be evaluated even when its author is not employed by a major university.

Recognition of Different Outputs

Scientific value is not limited to conventional journal articles. Software, datasets, monographs, reviews, replications, and research infrastructure may also matter.

Continuous Reassessment

A researcher’s evaluation can change as new work is produced and new evidence becomes available.

Global Participation

Scientific knowledge crosses national and institutional borders. A global funding mechanism may identify valuable work that local institutions have overlooked.

AIIM Must Not Become an Algorithmic Authority

AI-assisted evaluation introduces serious limitations.

An AI system can misunderstand specialized work, reward easily measurable outputs, overlook unconventional contributions, reproduce biases in its data, or generate confident explanations unsupported by evidence.

AIIM is therefore experimental. Its assessments should be interpreted as heuristic evaluations rather than definitive measurements of a scientist’s worth.

A credible system requires:

• transparent evaluation criteria;
• traceable source evidence;
• protection against manipulated profiles;
• procedures for correcting missing information;
• appeals and dispute resolution;
• human governance where automation is unreliable;
• adversarial testing;
• comparison with alternative allocation methods;
• public reporting of failures and limitations.

AIIM should be judged by its observed results: whether it identifies valuable contributors, allocates money fairly, resists manipulation, and improves the production of useful knowledge.

The purpose is not to replace academic authorities with an unquestionable algorithm. It is to develop a more open, evidence-based, and auditable method of scientific support.

Funding Scientists Without Creating Celebrities

Recognizing scientists does not require creating a cult of personality.

Recognition should follow documented contribution rather than fame, popularity, nationality, or institutional rank. Funding a person does not imply endorsing every claim that person makes.

Several distinctions remain essential:

• recognition is not proof;
• funding is not scientific verification;
• popularity is not scientific merit;
• an important scientist can still be mistaken;
• individual credit should not erase collaborators;
• team recognition should not erase individual contributions.

The objective is not to declare certain scientists inherently superior. It is to direct resources toward people whose documented work contributes to shared knowledge.

Science Is Shared Capacity

A scientific donation is often described as charity toward an individual researcher. That description is too narrow.

Well-directed scientific support can be:

• investment in humanity’s stock of knowledge;
• maintenance of intellectual infrastructure;
• support for future technologies;
• protection of neglected research;
• expansion of participation in science;
• funding for work whose benefits cannot be privately captured;
• a contribution to future generations.

Not every funded project will succeed. Some research will produce negative results. Some evaluations will be mistaken. Some discoveries will have little practical use.

These limitations do not eliminate the common-good character of science. They show why evaluation, transparency, experimentation, and diversified funding are necessary.

Help a Scientist, Help Everyone

The immediate recipient of scientific funding may be one person. The eventual beneficiaries may be millions.

A scientist can transform financial support into knowledge. Knowledge can be copied, tested, improved, and transmitted. Other people can convert it into education, technology, medicine, policy, or further discovery.

When society gives a capable researcher the opportunity to work, the resulting knowledge can become available to everyone.

Learn more about AI Internet-Meritocracy, read the information prepared for donors, and review the project’s operation and limitations in the AIIM user guide.

Donate to Science DAO to support the development and testing of AIIM and future funding for scientists and free-software developers.