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
Requirements capture is arguably the most important step in software engineering, and yet the most difficult and the least formalized one [Phalp and Shepperd 2000]. Enterprises build information systems to support their business processes . Software engineering research has typically focused on the development process, starting with user requirements—if that—with business modeling often confused with software system modeling [Isoda 2001]. Researchers and practitioners in management information systems have long recognized that understanding the business processes that an information system must support is key to eliciting the needs of its users (see e.g., Eriksson and Penker 2000]), but lacked the tools to model such business processes or to relate such models to software requirements. Researchers and practitioners in business administration have long been interested in modeling the processes of organizations for the purposes of understanding, analyzing, and improving such processes [Hammer and Champy 1993], but their models were often too coarse to be of use to software engineers. The advent of ecommerce and workflow management systems, among other things, has led to a convergence of interests and tools, within the broad IT community, for modeling and enabling business processes. In this article we present an overview of business process modeling languages. We first propose a categorization of the various languages and then describe representative languages from each family.
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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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