The Effect of Domain Familiarity on Modelling Roles: an Empirical Study.
Bibliographic record
Abstract
Conceptual modelling (CM) involves analysts working with domain experts to create a representation of the domain called a conceptual model. We address two issues of CM research. The first deals with the meaning that conceptual models convey. We propose guidelines for how analysts can reflect the concept of a “role” in a conceptual model using the extended entity relationship (EER) method. Roles are important in organizations, but analysts have little guidance about how to model them. The second issue focuses on the effect of prior domain familiarity of the users on the understanding of conceptual models. We conducted a laboratory study to determine how domain familiarity affects users’ understanding of conceptual models that represent roles. Our results indicate that conceptual models can be developed to show roles more clearly but that the benefit of doing so depends on model readers’ familiarity with the modeled domain. In particular, our guidelines will be most useful when users have moderate knowledge of the domain shown in the model. When users are very familiar with the domain, the guidelines do not seem to have much benefit. However, when users have very little knowledge of the domain, the guidelines do help to a certain extent.
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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 | 0.032 | 0.300 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".