Daejeon, July 27–31
The Music Encoding Initiative (MEI) has matured into a robust standard for the representation and analysis of musical texts, yet its implementation has largely remained concentrated within expert-led research projects housed in higher education or similar institutions (Grimmer, 2022; Kijas et al., 2023). These projects typically rely on institutional resources unavailable to most community-based cultural initiatives or educational settings outside universities (Duguid, 2020, 2021). At the same time, advances in tooling, exemplified by the widely-adopted MEI-friend, have lowered some of the technical barriers to entry (Goebl et al., 2024; Sapov, 2022). These advances allow new possibilities for integrating encoding into new pedagogical settings and for participatory and crowdsourced approaches (Kijas, 2020).
This paper proposes a citizen science approach and strategic coalition building with new partners as a means to rethink expanding inclusivity for music encoding projects (Edmond, 2021; PARTHENOS, 2019). Rather than treating encoding as a specialist activity that precedes interpretation, citizen-science approaches position encoding itself as a site of learning, collaboration, and knowledge production (Dobreva et al., 2015; Jennings et al., 2018). We offer two case studies that explore how MEI can function across educational, cultural, and national boundaries (Star & Griesemer, 1989; Star, 2010).
Existing efforts have effectively functioned to train new generations of encoders and have produced innovative research projects (Kijas et al., 2023). However, they implicitly reproduce boundaries around who can contribute to initiatives and the types of music and communities represented in such projects (Duguid, 2020; Neumann, 2024). Citizen science provides an alternate model that emphasizes participation and collective stewardship, enabling contributors to engage meaningfully without requiring full disciplinary mastery (Edmond, 2021; European Citizen Science Association, 2015). The following two case studies challenge such approaches: one bringing high school students into encoding Appalachian vernacular music and the other soliciting crowdsourced encoding (European Commission, 2018; Fenlon et al., 2023).
The case studies necessarily foreground questions that are frequently secondary in expert-driven projects: How do contributors enter and exit a project? How can community values be reflected in encoding and documentation choices while maintaining long-term scholarly usability? How is credit assigned? How are errors identified and corrected? (Carletti et al., 2013; Neumann, 2024, 2025; Zourou et al., 2022). Figure 1 shows the flexibility of assigning credit, tasks, and linking persons and institutions to authority databases (like ORCID) within an MEI file.
Figure 1 – <meiHead> snippet documenting persons, institutions, roles, and authority data links.
The first case study employs MEI within U.S. secondary education through a project focused on Appalachian vernacular musical traditions. Folklorists, archivists, and community organizations have documented this tradition for well over a century, generating collections held in both local and national institutions (UNESCO, 2003). Yet despite the rich history of documentation, nearly all records remain difficult to access, reuse, or analyze computationally (Duguid, 2021).
In this project, high school students will serve as encoders, learning technical and analytical skills by encoding locally grounded musical materials using MEI. Encoding activities are embedded within a broader curriculum that emphasizes cultural context, interpretive decision-making, and ethical responsibility (Grace et al., 2021; Land, 2013; Kashaka, 2024). Students work with digitized sources to produce MEI files and associated metadata, situating technical labor within a living musical ecosystem (Kijas, 2020).
Given the field’s history of extractive ethnographic practices, a central research focus concerns how consent, credit, withdrawal, and community review can be operationalized within a pedagogical encoding workflow (Dobreva et al., 2015; Digital Library Federation, 2022). Through participatory design and classroom pilots, the project develops an annotated consent and governance toolkit, including teaching-facing decision trees that balance student privacy, community authority, and preservation requirements (Fenlon et al., 2025). These materials aim to model how citizen-science encoding can be both ethically grounded and technically sustainable.
The second case study examines the development of crowdsourced MEI workflows designed for broader public participation. Supported by public-engagement–oriented funding, this work explores how simplified interfaces, modular training materials, and layered participation models can enable non-expert contributors to engage with music encoding (Carletti et al., 2013; PARTHENOS, 2019). This project explicitly aims to support international collaboration and reuse, breaking out of national boundaries (Zourou et al., 2022).
Here, citizen science permeates all specified roles: participants can contribute through micro-tasks such as verification or alignment, others take on more sustained encoding, documentation, or quality-assurance responsibilities (European Citizen Science Association, 2015; Duguid et al., 2019). Governance structures address attribution, contributor recognition, and error correction, closely paralleling challenges encountered in the secondary-education context despite the different institutional setting (Neumann, 2024; Fenlon et al., 2023). Together, these models suggest shared design principles for citizen-oriented MEI infrastructures.
Building on the ongoing work of the MEI Pedagogy community, this paper frames these initiatives as an opportunity to expand existing pedagogical collaborations. It invites educators, technologists, and cultural practitioners engaged in citizen science, crowdsourcing, secondary education, and public scholarship to work together within established MEI forums, extending who participates in MEI projects and how participation is supported. By breaking MEI out of the boundaries around higher education, we argue for a more inclusive, ethical, and scalable future for music encoding, shaped through coalition and shared practice.
The author(s) are grateful for cooperation of the EdVenture Group and the Bridging Innovation Institute.