Bibliographic record
Abstract
Purpose The paper aims at providing a conceptual framework based on clearly defined concepts and notions, which integrates goals into process modeling and specifically distinguishes goals from soft‐goals or business measures. The application of this framework facilitates a systematic use of soft‐goals in process design. Design/methodology/approach The framework is developed on the basis of Bunge's well‐established ontology. It is applied to processes taken from the SCOR supply chain reference model for demonstration and evaluation. Findings Applying the framework to the SCOR processes resulted in a set of focused relations between soft‐goals and processes, as opposed to the ones suggested originally in the SCOR model. This demonstrates the usefulness of the framework in process design. Research limitations/implications The approach presented in the paper is still rather a theoretical framework than a fully validated procedure. It should be tested on larger‐scale cases in more practical settings and evaluated accordingly. Practical implications Applying the clearly defined concepts of the framework and the suggested analysis procedure is expected to lead to focused and applicable measures tied to business process during process design, and provide a basis for process measurement requirements to be supported by an information system. Originality/value The contribution of the paper is both theoretical and practical. It provides clear‐cut ontology‐based definitions to concepts which so far have been assigned fuzzy and ambiguous meaning and uses these definitions for systematically tying business measures to business processes.
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.
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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".