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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".