An exploration of the many ways to approach the discipline of enterprise architecture
Bibliographic record
Abstract
Despite growing interest in enterprise architecture (EA) around the world in recent years, a lack of common understanding is frequently described by EA researchers/practitioners. We conducted a systematic mapping study and it revealed that the extent to which the authors/researchers are focused on EA, the sectors in which they are working, the academic disciplines in which they have studied, the countries where their affiliated organizations are located, the subject areas of the journals/publishers of their publications and the way they have approached EA and its practitioners are some major elements that might influence the existing uniformity in EA. In addition, this study demonstrates how important it is to pay attention to the definition of ‘enterprise architecture’ itself. The contribution of this study is the organization of the EA literature according to three major questions concerning ‘who’ have been published in the literature, ‘where’ they have been located and ‘what’ their publications are about. This helps to better identify sources of variety which could be on the basis of the lack of common understanding in EA and provides practitioners and stakeholders a better understanding of this challenge. This also provides relevant directions for future studies.
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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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.025 | 0.028 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".