Guidelines for Empirical Evaluations of Conceptual Modeling Grammars
Bibliographic record
Abstract
Conceptual modeling grammars are used to create scripts that represent someone’s perception, or some group’s negotiated perception, of domain semantics. For many years, researchers have evaluated conceptual modeling grammars to determine ways that they can be improved. One way to evaluate them is to empirically evaluate the strengths and weaknesses of the grammars in terms of their effectiveness and efficiency in generating scripts. A number of researchers have proposed guidelines for the design of empirical research to conduct such evaluations. Although these guidelines have proved useful, further clarification is needed in relation to (1) criteria for evaluating grammar performance, (2) characteristics of grammars that can influence grammar performance, and (3) factors that must be considered when testing the effect of grammar characteristics on grammar performance. We review past conceptual modeling research and provide guidelines for addressing these three issues. We also illustrate how the guidelines would apply to studies that evaluate conceptual modeling grammars from an ontological perspective. Finally, we discuss how the guidelines extend those offered in past research and the implications of our work for future research.
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.
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 | 0.437 | 0.741 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.023 | 0.023 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".