An activity-object world view for ABCmod conceptual models
Bibliographic record
Abstract
In spite of its considerable intuitive appeal, an activity oriented perspective for model development has largely been ignored in the modeling and simulation community. Recently, however, the naturalness of the activity perspective provided the basis for the development, by the authors, of a comprehensive, flexible but descriptive conceptual modeling environment; namely, the ABCmod framework (ABCmod = Activity Based Conceptual modeling) [1, 2, 4]. A new world view called the Activity-Object World View has emerged from this previous work which facilitates the transformation of an ABCmod conceptual model into a simulation model. The key feature of this world view is that the activity is treated as an object in an object-oriented (OO) programming paradigm. The ABSmod/J package (ABSmod = Activity Based Simulation Modeling with Java) has been developed to create simulation models based on this world view and its features are outlined in this paper. Unlike traditional world views, the approach does not extract, and separately manage, the underlying events but rather retains their existence as integral parts of activity objects.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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; 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".