Understanding Representation Fidelity: Guidelines for Experimental Evaluation of Conceptual Modeling Techniques
Bibliographic record
Abstract
Recently, there has been a resurgence of interest in experimental research on conceptual modeling in information systems analysis and design.There is a need to explicitly identify the objectives of specific experiments in this area, and the role that assumptions play in experimental design.We provide four guidelines for developing materials for experiments aimed at evaluating conceptual modeling techniques, based on the premise that the primary purpose of conceptual modeling is to facilitate communication between analysts and users in validating domain knowledge relevant to an information system.We offer the guidelines as recommendations to assist the development of experiment materials that support meaningful tests of domain semantics, and present empirical evidence to illustrate the value of two of the guidelines.We also evaluate the degree to which a selection of recent experiments on conceptual modeling adheres to the guidelines, and consider implications of that assessment.
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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 | 0.359 | 0.699 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".