Investigation of the Lack of Common Understanding in the Discipline of Enterprise Architecture : A Systematic Mapping Study
Bibliographic record
Abstract
The number of publications, along with the organization of new conferences are a couple of the relevant elements that usually indicate the progress of an area of study over the years. This is definitely true in the case of the Enterprise Architecture (EA) discipline, which went from having its first journal article published in 1989 to over two hundred published articles by 2015. But in spite of this evolution, EA is still suffering from a considerable lack of common understanding. It has become very important to investigate the current state of affairs concerning the EA discipline through its relevant publications in order to shed some light on this challenge. 171 journal papers published between 1990 and 2015 were systematically selected and examined in order to accomplish this investigation. The quantitative and qualitative findings of this examination show that EA is a young discipline which raises a growing interest in recent years. This examination also confirms the lack of common understanding in EA, which can be observed in the different descriptions of the term "enterprise architecture," and in the diversity of perspective with regards to the whole discipline. Several issues related to this lack has been reported, such as multidisciplinary issue, language issue, structure of research and mode of observation issues. The major issue concerns the absence of enough research to shed some light on this challenge. In addition to this investigation, helpful directions for future research in this area was proposed.
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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 | 0.050 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.043 | 0.034 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".