Bibliographic record
Abstract
If tangible user interfaces (TUIs) are going to move out of research labs and into mainstream use they need to support tasks in abstract as well as spatial domains. Designers need guidelines for TUIs in these domains. Conceptual Metaphor Theory can be used to design the relations between physical objects and abstract representations. In this paper, we use physical attributes and spatial properties of objects as source domains for conceptual metaphors. We present an empirical study where twenty participants matched physical representations of image schemas to metaphorically paired adjectives. Based on our findings, we suggest twenty pairings that are easily identified, suggest groups of image schemas that can serve as source domains for a variety of metaphors, and provide guidelines for structuring physical-abstract mappings in abstract domains. These guidelines can help designers apply metaphor theory to design problems in abstract domains, resulting in effective interaction.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.040 | 0.009 |
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".