Popperian Falsifiability on Enterprise Architecture Is Suitable from a Scientific Standpoint?
Bibliographic record
Abstract
Enterprise architecture (EA) is defined as a high-level strategic modeling, which has been shaped to help managers deal with the complexity of the business environment. Just as many areas of knowledge have been the focus of researchers on what regards testing and verifying them as scientific or not, EA is the focus for the analysis conducted in this study. Among the many scientific demarcation criteria are the philosopher Karl Popper’s ideas, which only consider as scientific theories that can be properly tested and are falsifiable. This study aims to analyze how studies related to EA, considering Popper’s scientific demarcation criteria, contribute to the acknowledgement of EA as suitable from a scientific standpoint. In an extensive literature review, EA studies that focused on business management in international databases were sought after. The results, when analyzed under the rules that guide the methods used on EA studies, lead to the inference that despite having made great progress, EA still has a long way to go on the search for expansion and maturity of the analyzed criteria.
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.
How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
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 teacher head, 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".