Exploring Intentional Modeling and Analysis for Enterprise Architecture
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
An enterprise architecture is intended to be a comprehensive blueprint describing the key components and relationships for an enterprise from strategies to business processes to information systems and technologies. Enterprise architectures have become essential for managing change in complex organizations. While "motivation" has been recognized since Zachman 0 as an important element of enterprise architecture, yet to date, most enterprise architecture modeling only deals with structure, function, and behaviour, neglecting the intentional dimension of motivations, rationales, and goals. The contribution at hand explores this challenge and aims to illustrate the potentials of intentional modeling in the context of enterprise architecture. After introducing two intentional modeling languages and their potential relation to an enterprise architecture construction process, we report on an explorative case study that aimed to investigate the practical implications of intentional modeling and analysis for enterprise architectures. Finally, we present key observations from interviews that were conducted with practitioners to obtain feedback regarding the material developed in the case study.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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