Bibliographic record
Abstract
The desire to obtain value and justify investments from the different Information Systems in place in organizations has been around for a long time. Organizations constantly theorize and implement different approaches that provide some sort of alignment between their different business objectives and Information Systems. Unfortunately, the environments in which these organizations operate are often dynamic, constantly changing with influence from external and internal factors that require continual realignment of the Information Systems with business objectives to provide value. When businesses evolve, leading to changes in business requirements, it is hard to know what direct Information System changes are needed to respond to the new requirements. Similarly, when there are changes in the Information System, it is not often easy to discern which business objectives are directly affected. Whilst the different Enterprise Architecture frameworks available today provide and propose some form of alignment, in their implementation, they do not show links between business objectives and Information Systems, i.e., indicating what Information System is directly responsible for different business objectives thereby allowing for anticipation and support of changes as the business evolves. This thesis utilizes insights from Business Intelligence and uses the User Requirements Notation (URN), which enables modeling of business processes and goals, to provide a framework that exploits links between business objectives and Information Systems. This Business Intelligence - Enabled Adaptive Enterprise Architecture framework allows for anticipating and supporting proactively the adaptation of Enterprise Architecture as and when the business evolves. The thesis also identifies and models levels within the enterprise where responses to change as the business evolves are needed and the ways the changes are presented. The tool-supported framework is evaluated against the different levels and types of changes on a realistic Enterprise Architecture at a Government of Canada department, with encouraging results.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".