Digital Making: 3D printing and artisanal glass production
Bibliographic record
Abstract
Direct digital design and additive manufacturing are enabling new pathways for the design, development and distribution of material goods – radically redefining existing sites for knowledge exchange and our core assumptions of what makes a contemporary material practice. In the era of open source, democratized production, the relationships between an object, how it is made, what it is made of, where it is made, by whom and when, are directed by the maker. For the last forty years, 3D printing has been used as an ideation tool to model what could be. The steady emergence of Direct Digital Manufacturing (Singer P. et al. 2011) has enabled us to manipulate true-life materials to directly achieve the final object. This paper will focus on emergent modes of making using legacy materials, leveraging work done in foundry and ceramics into glass, and how 3D printing provides room for innovation not only with these materials, but also with the requisite digital processes in terms of software, hardware, and workflow opportunities. This design-led creative research looks at opportunities for innovation in material practice and also seeks out the affinities and opportunities, which arise when design methodologies are implemented alongside an artisanal, craft-based approach to making.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".