Lessons from a conceptual modeling exercise
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
There is considerable need to educate students in the process of developing conceptual models within the modeling and simulation discipline. The challenge is complicated by the fact that the specific form of a conceptual model is not driven by universally accepted criteria and one might argue that the ultimate purpose of such a model is itself ill-defined. In 2010 one of the co-authors initiated a Conceptual Modeling Corner segment in the Society of Modeling and Simulation International's M&S Magazine. To focus the discussion, a specific problem was outlined and readers were encouraged to propose conceptual models for the problem. The M&S course within the Georgia Tech Professional Masters in Applied Systems Engineering program recently used the problem as an assignment and 46 students developed conceptual models. In this paper we outline criteria developed to evaluate a subset of these conceptual models and a number of lessons learned from this exercise.
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) | 0.004 | 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 it