Are All Classes Created Equal? Increasing Precision of Conceptual Modeling Grammars
Bibliographic record
Abstract
Recent decade has seen a dramatic change in the information systems landscape that alters the ways we design and interact with information technologies, including such developments as the rise of business analytics, user-generated content, and NoSQL databases, to name just a few. These changes challenge conceptual modeling research to offer innovative solutions tailored to these environments. Conceptual models typically represent classes (categories, kinds) of objects rather than concrete specific objects, making the class construct a critical medium for capturing domain semantics. While representation of classes may differ between grammars, a common design assumption is what we term different semantics same syntax (D3S). Under D3S, all classes are depicted using the same syntactic symbols. Following recent findings in psychology, we introduce a novel assumption semantics-contingent syntax (SCS) whereby syntactic representations of classes in conceptual models may differ based on their semantic meaning. We propose a core SCS design principle and five guidelines pertinent for conceptual modeling. We believe SCS carries profound implications for theory and practice of conceptual modeling as it seeks to better support modern information environments.
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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 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.021 | 0.064 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".