Learning by watching Vernacular Iñupiaq-Inuit design learning as inspiration for design education
Bibliographic record
Abstract
In this article, I explore a single case of vernacular clothing design — the practice and learning of design for contemporary Iñupiaq-Inuit clothing made by women from Kaktovik in Northern Alaska — and I hope to contribute to a better understanding of design practice and learning in general. Design research has many unexplored areas, and one of these omissions is vernacular design, or folk design. In my opinion, professional and academic design may well have something to learn from vernacular design, although this research is about vernacular learning and about what, why and how the ‘making’ discipline of clothing design is learned. This study was based on observations of and interviews with seamstresses and research-by-design, which includes authorial participation in designing and sewing in adherence to Iñupiaq tradition. All of this was recorded on digital video film. The investigation of Iñupiaq-Inuit clothing design indicates that watching was the most common way of learning, a phenomenon I have chosen to call learning-by-watching, a concept that can be seen as a development of both Schön and Wenger’s theories of learning, as influenced by John Dewey’s theory of learning-by-doing. This study will be discussed in connection with design education, from kindergarten to professional studies in higher education, in the forthcoming research project, Design Literacy, the purpose of which is to develop theory to improve design education in both compulsory and academic design education. Consequently, to improve design education in general, a thorough focus on learning-by-watching in communities of practice would make for more reflective practitioners and more sustainable design practices in the long run. Keywords: Vernacular design, clothing design, design thinking, learning-by-watching, learning-bydoing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".