Enterprise Architecture Measurement: An Extended Systematic Mapping Study
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
A systematic mapping study (SMS) of proposed EA measurement solutions was undertaken to provide an in-depth understanding of the claimed achievements and limitations in evidence-based research of enterprise architecture (EA). This SMS reports on 22 primary studies on EA measurement solutions published up to the end of 2018. The primary studies were analyzed thematically and classified according to ten (10) mapping questions including, but not limited to, positioning of EA measurement solutions within EA schools of thought, analysis of consistency-inconsistency of the terms used by authors in EA measurement research, and an analysis of the references to the ISO 15939 measurement information model. Some key findings reveal that the current research on EA measurement solutions is focused on the -enterprise IT architecting school of thought, does not use rigorous terminology as found in science and engineering, and shows limited adoption of knowledge from other disciplines. The paper concludes with new perspectives for future research avenues in EA measurement.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.008 |
| Open science | 0.001 | 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