Customizing the Representation Capabilities of Process Models: Understanding the Effects of Perceived Modeling Impediments
Bibliographic record
Abstract
Process modeling is useful during the analysis and design of systems. Prior research acknowledges both impediments to process modeling that limits its use as well as customizations that can be employed to help improve the creation of process models. However, no research to date has provided a rich examination of the linkages between perceived process modeling impediments and process modeling customizations. In order to help address this gap, we first conceptualized perceived impediments to using process models as a “lack of fit” between process modeling and another factor: 1) the role the process model is intended for; and 2) the task at hand. We conducted a case study at two large health insurance carriers to understand why the lack of fit existed and then show different types of process modeling customizations used to address the lack of fit and found a variety of “physical” and “process” customizations employed to overcome the lack of fits. We generalize our findings into propositions for future research that examinethe dynamic interaction between process models and their need to be understood by individuals during systems analysis and design.
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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 | 0.050 | 0.316 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".