Shielin-bough: Building Collaboration Across the North
Bibliographic record
Abstract
Our article explores inter-cultural and interdisciplinary collaboration between the Glasgow School of Art in Scotland and the University of Lapland in Finland. We focus on developing sustainable practices in Arctic art education and enhancing understanding of the challenges faced by Arctic regions and northern residents, including the loss of traditional ways of life, climate change impacts and cultural shifts. We leverage art education to connect diverse cultures and to innovate within Arctic traditions, offering a creative platform in higher education collaboration. This article articulates the relevance of transcending geographical, disciplinary, cultural, and temporal borders for cultural preservation and environmental awareness. In the article, we introduce the Shielin-Bough project, dividing it into four phases: research, co-design, implementation, and reflection. In the local activities, student research, and fieldwork, we focus on traditional shelters, storytelling, and intangible heritage of food. In the first phase of the article, we view interpretations of cultural traditions and aspects of community life in Finland and Scotland, drawing inspiration from two traditional shelters: the Finnish laavu and the Scottish shieling. In the second phase, we focus on the co-design workshop at The Glasgow School of Art’s Highlands & Islands campus, utilizing the Miro board which served as a collaborative online platform for dialog between architecture, art education, and design students from Scotland, Finland, and China. In the third phase we present the practical field school where the designed artifact – the combination of the traditional shelters – and related decorations were constructed in a new context, highlighting the project’s cross-border nature and sustainable living practices. Our results highlight the potential of cross-border collaboration in Arctic art education to create meaningful connections and sustainable practices by demonstrating innovative ways to integrate the traditional and the contemporary. The blending of cultural aspects gives the project a unique significance, as it introduces something novel to the Scottish landscape. The constructed artefact was recognised for its inclusive approach, suggesting a sense of community and collective involvement and benefit, enhancing the landscape for everyone, not just those directly using it.
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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 | 0.019 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.014 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".