Towards the adoption of international standards in enterprise architecture measurement
Bibliographic record
Abstract
Literature on Enterprise Architecture (EA) report that EA is an emerging discipline with an increasing attention from both academia and industry. However, the literature report on some challenges in EA research. For instance, EA modelling and EA measurement. In this paper, we aim to assist the EA community to overcome the challenges found in EA measurement, and enhance the adoption of knowledge from mature disciplines. Therefore, and to our knowledge, this paper is the first attempt to adopt two (2) international standards: ArchiMate as a standard language for EA modelling, and Common Software Measurement International Consortium (COSMIC) as a measurement method standard. The paper outlines the adoption (referred to as mapping process), and propose accordingly a novel EA measurement approach based on these two (2) international standards. Since the proposed approach is based on recognized international standards, it is expected that the approach can be handy for EA practitioners, and easy to adopt by organizations. The paper describes a demonstrative example from the insurance industry using the novel measurement approach, and concludes with future research avenues.
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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 | 0.162 | 0.189 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.013 |
| 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".