Demonstrating FEDT: Supporting Characterization Experiments in Fabrication Research
Bibliographic record
Abstract
Recent fabrication research across HCI and graphics shows an incredible diversity of work, much of which features characterization experiments. However, performance and reporting of these experiments are wildly inconsistent, not only reducing transparency that reassures reviewers and readers of a project’s rigour but also challenging a technique’s replicability by future researchers. We propose building a domain-specific language (FEDT: Fabrication Experiment Design Tool) which can express a wide variety of such characterization experiments, and which can be extended to many different machines. This language is sufficiently expressive to describe many types of experiments (3D printing, lasercutting, post-processing), including ones which require human intervention in their steps. We replicate classic fabrication experiments in FEDT to demonstrate its flexibility and discuss the importance and applicability of domain-specific languages and tools to Open Science.
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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 | 0.039 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".