Multi-stakeholder platforms (MSPs) coordinate diverse stakeholder groups, often with competing or conflicting requirements. As these platforms increasingly take digital form, engineers building them make architectural decisions about data visibility, service decomposition, and algorithm design that directly determine which stakeholder requirements are prioritized when conflicts arise. Software architecture literature provides patterns for data isolation and access control among tenants but does not address how architectural decisions resolve conflicts among stakeholders with structurally divergent interests. MSP governance literature identifies the principles at stake but treats technology as neutral infrastructure. Neither addresses the translation between governance principles and architectural decision spaces. This paper proposes a governance-architecture correspondence framework that surfaces implicit governance decisions, making them explicit and debatable before deployment. The framework maps five MSP governance principles to the architectural decision spaces where they must be addressed, identifying for each the governance-aware design choice and the technically convenient default it overrides. We illustrate the framework in a constructed knowledge platform for pig farming in Rwanda, where five stakeholder types present structurally conflicting requirements. As work in progress, the framework is proposed but not yet empirically validated; a planned pre/post judgment study with platform users across all stakeholder types will test falsifiable predictions about governance outcomes.

翻译：多利益相关方平台协调来自不同利益相关群体的需求，这些群体往往存在相互竞争或冲突的需求。随着此类平台日益数字化，构建平台的工程师需在数据可见性、服务分解和算法设计等方面做出架构决策，这些决策直接决定了冲突发生时哪些利益相关方的需求将被优先满足。软件架构文献提供了租户间数据隔离和访问控制的模式，但并未涉及架构决策如何解决结构利益分歧的利益相关方之间的冲突。多利益相关方平台治理文献虽指出相关治理原则，却将技术视为中立基础设施。然而，现有研究均未探讨治理原则与架构决策空间之间的转化机制。本文提出一种治理-架构对应框架，该框架将隐含的治理决策显性化，使其在部署前即可被明确讨论与辩驳。该框架将五项多利益相关方平台治理原则映射至其必须解决的架构决策空间，并为每项原则指明具有治理感知的设计选择及其所替代的技术便捷默认方案。我们以卢旺达养猪知识平台为例展示该框架——该平台涉及五种存在结构性需求冲突的利益相关方类型。作为研究进展，本框架虽已被提出但尚未经过实证验证；计划后续通过覆盖所有利益相关方类型的平台用户前后对比判断研究，检验关于治理结果的可证伪预测。