Capitalizing on Complexity in Modern Business Environments: A Network-Based Perspective for Projects and Organizations
Bibliographic record
Abstract
Traditional approaches to design organizations are insufficient to withstand the constraints imposed by modern business environments that are in permanent transformation with a constant flow of information, interactions and behaviors of agents, and the non-linear relationship between resources and products. By building on the nature and dynamics of Networks, it is possible ideating organizational architectures and processes that capitalize on the complexity emerged from modern economies. These novel architectures have a significant potential to induce self-adaptation and co-evolutionary processes between agents, organizations and the external environment. The design of organizations able to navigate complex environments requires the adoption of a conceptual framework that accounts for the strategic characterization of the Network agents and the adoption of an open-systems perspective that guides the effective interaction with the external context. This allows optimizing information assimilation and knowledge production processes that will drive the ideation of strategic scenarios. As a result, the organization will increase its potential to synchronize adaptation with its business ecosystem and its readiness for strategic transformation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".