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
AbstractIn today's rapidly evolving world, companies need to constantly adjust their business models to changes in their environment. A good approach to evolving business models strikes a balance between capitalizing on new opportunities, and preserving investments in existing business processes. In this chapter, we argue that the User Requirements Notation (URN) provides such an approach. URN supports the modeling and analysis of user requirements in the form of goals and scenarios. Goals can be used to model high-level business (as well as system-level) objectives, and scenarios to describe the business processes to meet those goals. The approach is lightweight, and allows the quick evaluation of business model alternatives. Business models are represented in terms of actors and their dependencies, which correspond to value flows between the actors. Those value flows can subsequently be refined into business process activities. The approach gives business managers a tool for the systematic and incremental evolution of business model alternatives for their organizations. It allows them to model the strategic options available to them, and the conditions for their successful application.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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