An Experimental Study of the Effects of Representing Property Precedence on the Comprehension of Conceptual Schemas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Conceptual modeling is the process of using a grammar to construct abstractions of relevant phenomena in a domain. The resulting conceptual schemas are intended to facilitate understanding of and communication about a domain during information systems requirements analysis and during design. Despite keen practitioner interest in conceptual modeling, there is general agreement that the modeling constructs comprising grammars lack theoretical foundations pertaining to what the constructs are intended to represent, which, in turn, inhibits our understanding of whether and why they are effective. This research contributes to our understanding of conceptual modeling grammars by proposing a theoretically-grounded approach for modeling an important aspect of the nature of properties of the phenomena of interest in a domain. Specifically, conceptual schemas typically fail to express explicitly the semantics that, when things possess particular properties, they must also possess certain other properties. This research uses Bunge’s ontological notion of property precedence as the theoretical rationale for explicitly modeling this dependence in conceptual schema diagrams. We examine several forms of precedence, and propose an approach to representing one form in conceptual schemas. We present the results of a laboratory experiment that tests the impact of explicitly representing precedence on how well participants comprehend the semantics conveyed by a conceptual schema. The results indicate that modeling precedence explicitly improves the comprehension of domain semantics expressed in a diagram’s structure, but has varying effects on subjects’ confidence in their comprehension.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it