International scientific collaboration is valuable when partners contribute complementary knowledge, facilities, data, or perspectives. But making foreign partners a condition of funding can create a perverse incentive: researchers must assemble an eligible consortium even when their research does not need one.
Funding rules should reward the value of collaboration, while preserving a credible path for research that does not require international partners.
UNESCO’s goal: open and equitable cooperation
UNESCO’s 2021 Recommendation on Open Science promotes international cooperation to reduce technological and knowledge gaps. It also emphasizes equity, inclusion, and flexibility, recognizing differences among research communities. These principles support making cooperation easier and more accessible. They do not establish a universal requirement that every researcher recruit foreign collaborators to receive funding. See the UNESCO Recommendation on Open Science.
The criticism therefore concerns how funding programmes structure eligibility. It should not imply that UNESCO created particular consortium rules or that those rules resulted from its recommendation without evidence establishing that connection.
When collaboration becomes an admission ticket
A concrete example is Horizon Europe. The European Research Executive Agency explains that most calls require at least three partner organisations from three different EU or associated countries, including at least one partner from an EU country. Exceptions and additional conditions depend on the call. These are organisational requirements, not simply a demand for three individual co-authors. See the official Horizon Europe participation guidance.
Such requirements can serve legitimate objectives: combining facilities, coordinating research across borders, and building international capacity. The difficulty arises when a valuable project fits the scientific topic but has little substantive need for the required partnership structure.

Consider a hypothetical mathematician developing a proof that primarily requires time and computing resources. If the relevant funding route requires a multinational consortium, the researcher must find institutions, negotiate responsibilities, and construct a joint proposal before the work can receive support through that route.
The eligibility test then measures something beyond research merit: the ability to assemble an administratively acceptable network.
Why compulsory partnerships can encourage token participation
A mandatory partner requirement creates a plausible incentive to recruit an organisation partly—or mainly—to satisfy eligibility conditions.
There are important distinctions:
- Substantive collaboration: a partner contributes expertise, infrastructure, data, validation, or another useful capability.
- Limited but legitimate participation: a partner performs a small, necessary role, honestly described and appropriately funded.
- Token participation: a partner is included primarily to complete the required consortium structure, with little added value.
- Misrepresented participation: claimed contributions do not reflect the work actually performed.
A small role is not automatically a fake role. Nor do consortium rules, by themselves, prove that token or misrepresented participation is common. Establishing its prevalence would require audits, surveys, or project-level evidence. The defensible criticism is that these rules can create incentives for it.
The cost is more than paperwork
Where additional partners add little value, the potential costs include time spent negotiating artificial work packages, coordination expenses, and delays before research begins. Researchers without established international networks may also face an additional barrier unrelated to the quality of their ideas.
There is a problem of dependence, too. An organisation needed to satisfy eligibility rules can gain bargaining power because its withdrawal may jeopardize the application.
Calling this “slavery” expresses frustration, but compulsory collaboration or funding-induced dependence describes the mechanism more precisely. Researchers remain formally free to decline; their practical alternatives may nevertheless be limited.
How funders can support genuine collaboration
Funders should distinguish projects that need international cooperation from projects that can succeed independently. Collaboration-focused programmes can coexist with accessible individual and small-team funding.
Within collaborative calls, evaluation should examine what each partner contributes and whether that contribution justifies the coordination cost. Capacity building, community engagement, and access to local knowledge can all be valuable contributions; direct authorship is not the only measure.
A useful evaluation question is: What scientific or public benefit would be lost if this partner were absent?
AIIM offers a different approach to test
AI Internet-Meritocracy (AIIM) proposes allocating support to researchers and open-source developers through AI-assisted assessment of their existing contributions. This offers a route for exploring funding that does not depend on assembling a multinational grant consortium.
Its potential advantage here is researcher autonomy: people can collaborate because the work benefits from it. However, AIIM’s assessment accuracy, fairness, and resistance to manipulation require validation. Removing consortium requirements does not, by itself, demonstrate a better allocation of funding.
The policy objective should be freedom to cooperate meaningfully. International collaboration works best when funding helps researchers find useful partners—and also allows valuable research to proceed without unnecessary ones.
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AI Internet-Meritocracy (AIIM) is an operational beta designed to allocate available donations to researchers and open-source developers using AI-assisted evaluation of documented contributions. Payment transactions are already recorded on-chain.
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Independent review
Researchers and technical reviewers: independent criticism is welcome, including negative conclusions. Review AIIM’s assumptions, governance, failure modes, and testing plan →
Research status: AIIM remains experimental. AI-generated evaluations can contain factual errors or biases, and decentralized governance and non-custodial components remain under development. That uncertainty is why public testing, auditability, and external criticism are central to the project.