Translational craft: Handmade and gestural knowledge in analogue–digital material practice
Bibliographic record
Abstract
Abstract This article investigates how craft knowledge can be utilized and acquired in the handcrafting process using digital tools and digital fabrication methods. It is based on a study that seeks ways in which craft-making and handcrafted objects can be translated using digital technology and addresses the following questions: (1) What forms of knowing and meaning-making are evolving in a craftsperson working with digital means? (2) What does it mean to manipulate material in computer-aided design through virtual reality, and how does this inform analogue material practice and experimentation? The study was carried out through the author’s craft practice. Originating with a hand-knotted artefact, the author transformed this analogue form into digital form using a range of techniques. The activities act as both a survey of digital fabrication capabilities and a way of exploring new thinking mechanisms offered by this emerging form of practice. The study broadens our understanding of the craftsperson’s role within the capabilities and limitations of digital interface and tools. Several iterations of digitally fabricated objects were documented and reflected upon. This emerging craft practice acts as a catalyst for established disciplines within art and design to collide and interact. Outcomes of this study include mapping new workflows and the translation of gestures within digital and analogue material practice and reflection on how the materials and methods used in digital fabrication have the potential to expand the meanings connected to the things that are created. These outcomes evidence not only how the craftsperson utilizes her previously acquired knowledge in a new context of working with digital tools but also how she acquires new handmade knowledge through the act of translating analogue practice into a digital one.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".