Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Interest in understanding and facilitating 3D digital fabrication is growing in the HCI research community. However, most of our insights about end-user interaction with fabrication are currently based on interactions of professional users, makers, and technology enthusiasts. We present a study of casual makers, users who have no prior experience with fabrication and mainly explore walk-up-and-use 3D printing services at public print centers, such as libraries, universities, and schools. We carried out 32 interviews with casual makers, print center operators, and fabrication experts to understand the motivations, workflows, and barriers in appropriating 3D printing technologies. Our results suggest that casual makers are deeply dependent on print center operators throughout the process from bootstrapping their 3D printing workflow, to seeking help and troubleshooting, to verifying their outputs. However, print center operators are usually not trained domain experts in fabrication and cannot always address the nuanced needs of casual makers. We discuss implications for optimizing 3D design tools and interactions that can better facilitate casual makers' workflows.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it