Practical EA Model Development: A Case Study of An Educational Institution in Bahrain
Bibliographic record
Abstract
Organizations strive to meet their business goals in preserving a desired harmony and collaboration between its business environment and integrated ICT. At the same time, Enterprise architecture (EA), as a high ontological analysis tool, claims that organizations could directly benefit from EA efforts in enhancing knowledge and improved decision making about the organization‘s people, business processes, information, and ICT applications. Massive number of EA methodologies and frameworks assist organizations in achieving their aforementioned benefits. This paper addresses the development of an EA (baseline and a target architectural effort) analysis that enables the management of a Bahraini educational department; Information Systems Department (IS-Dep) assess its readiness for investing in a new Dashboard application. Respectively, Zachman Framework (ZFW), an Architecture Development Process (ADP), and ArchiMate modeling language were employed as an analysis tool, project methodology and a rigorous architecture description provider for the business and IT stakeholders. Results reveal that 1) The whole university is facing communication and information sharing difficulties, at which more advanced application systems, should be adopted to correct this problem. 2) Every type of data and information in the university is centralized implying that lengthy and time-consuming procedures are to be tackled. In order for the IS-Dep to meet the needs of both students and academics, the system needs to be decentralized. 3) Many services are not fully utilized, so the ISDep should utilize them.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.011 |
| Open science | 0.000 | 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 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".