Bibliographic record
Abstract
Enterprise Architecture (EA) is a consulting practice and discipline intended to improve the management and functioning of complex organizations. The various approaches to EA can be classified by how they define what is to be architected and what, as a result, is the relevant environment. Traditionally, management has been understood as “Planning, Organizing, Command, Coordinating, and Controlling” (POCCC), that is, the role is bounded within the organization. The corresponding EA approach suggests architecting IT systems to support management, with the implicit environment being members of the organization as well as partner organizations. As the objective of EA practice expands to include organizational members, technical systems, and a wider set of stakeholders, so too does the complexity it must address. This results in an enlarged domain of issues and concerns. Finally, if the objective of EA is a sustainable enterprise, then physical, societal, and ecological environments radically increase the complexity of actualizing this goal. Corresponding to this increase in scope is a parallel shift in the scope of management concerns. With the goal of pushing EA towards concerns regarding enterprise sustainability, an open socio-technical system design perspective of EA, which we have named Enterprise-in-Environment Adaptation (EiEA), is discussed. EiEA offers a comprehensive approach to respond to the demands for complexity management that arise when working towards enterprise sustainability; yet, it requires that organisations also embrace deep culture changes, such as participative design, worker empowerment, as well as shared accountability and responsibility, to name a few.
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 | 0.000 | 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 | 0.022 |
| 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".