Bibliographic record
Abstract
Digital desktop fabrication technologies such as 3D printing are currently being lauded in the popular press as a potentially socially transformative technology. We somewhat agree, arguing that 3D printing holds great socioeconomic implications, but also that more sustained attention should be paid to the ways in which 3D printing is entering into our creative environments. Our focus in this article is on the use of rapid prototyping by creatives such as architects, designers, and DIY advocates, since it is within these contexts where the popular themes of 3D printing are currently most concrete. To this end, in section one we provide some background for desktop digital fabrication, contextualizing 3D printing within industrial processes and Maker subcultures. In section two, we summarize our environmental scan of relevant popular and academic literature, using this to identify key trends in this area. We supplement this discussion in section three using our analysis of a ‘critical making’ session that took participants through a process of designing and printing simple objects as well as follow–up interviews with these participants. In the concluding section, we target four areas in need of future research.
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.049 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".