Improving Semantic Transparency of Committee-Designed Languages through Crowd-sourcing
Bibliographic record
Abstract
Committee-designed languages such as those of the OMG consortium are widely used in both industry and academia.These languages seem to be used increasingly by users with no technical background for the visualization, documentation and specification of workflows, data and software systems.However, according to several studies on these languages, the used visual notations do not seem to convey any particular semantics and the recognition of such notations is not perceptually immediate.This lack of semantic transparency increases the cognitive load to differentiate concepts from each other and slows down recognition and learning of the language constructs.This paper proposes a process, which leverages the crowd-sourcing to improve the semantic transparency of such languages.We believe that involving end-users in the design process of the languages visual notations should increase the expressiveness of these languages and then their acceptance for a wide range of novice-users.
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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 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".