Customizing the Representation Capabilities of Process Models: Understanding the Effects of Perceived Modeling Impediments
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
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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