Supporting Sustainable Island Tourism Through Infrastructuring Co-Design: A Case Study From Mayu Island
Bibliographic record
Abstract
Island tourism faces challenges due to evolving issues and limited participation from diverse and marginalised groups, undermining efforts towards sustainability. To address these challenges, this study advances a co-design approach that foregrounds the role of infrastructure as an enabling foundation for ongoing participation and collaborative innovation. It draws on a six-month co-design project on Mayu Island in Southeast China, a community in transition from fishing to tourism. Instead of directly proposing solutions or development ideas, the design teams engaged government officials, business owners, residents, and tourists through a set of socio-material arrangements, including mapping, an interactive exhibition, and co-design workshops. This process yielded two collaboratively developed solutions: a new cleaning schedule to address beach litter and the repurposing of an underutilised parking lot to support vendors. Building on these outcomes, a co-design infrastructure is proposed, comprising three interconnected components: mapping (surfacing complex relationships), narrating (embedding lived narratives), and deriving (enabling exploration of new possibilities). The findings demonstrate how the co-design infrastructure process brings diverse voices to the surface, fosters collective meaning-making, and supports adaptive problem-solving. Furthermore, the features of islandness specific to Mayu Island are analysed to highlight how they shape co-design strategies and outcomes, reinforcing the importance of context-sensitive approaches in island settings. Ultimately, the study demonstrates how co-design infrastructure processes can support more grounded and enduring forms of island tourism development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".