Modeling e-government business processes: New approaches to transparent and efficient performance
Bibliographic record
Abstract
Many governments worldwide are restructuring their business practices to improve their performances. To help describe and understand the process of restructuring, modeling techniques are used at different levels of modeling abstraction. This paper presents a new approach to the modeling of e-government business processes. It is based on two existing modeling techniques: Business Process Mapping (BPMapping) and UN/CEFACT Modeling Methodology (UMM). The BPMapping technique provides an overall graphical representation of an organization depicting all different types of business processes, their inputs, outputs, and the environment in which the organization operates. The UMM methodology with its different business views gives details on collaborations and interactions of business processes. Combining BPMapping with UMM leads to a very expressive modeling approach which can provide artifact details at the higher levels of modeling abstraction and which also shows deployment strategies of the business processes. To illustrate the effectiveness of the proposed approach, it is applied to the modeling of the Record Integrated Management business process at Quebec Government.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".