Tech Tides: Steering Through Cooperative Complexities with the Institutional Role Model as an Economic System Architecture
Bibliographic record
Abstract
The Institutional Role Model (IRM) is a versatile tool used as an economic system architecture in various projects, such as the Gaia-X 4 Future Mobility lighthouse project family.This paper examines the effects of digitalization and geo-economic changes on cooperative instruments and demonstrates how the IRM can be optimized to meet these new requirements.The proposed optimizations include implementing the roles of Chief Innovation Officer, Sustainability Manager, AI Manager, and Remote Work Manager.Furthermore, a new prioritization according to Very Important Roles, Essential Roles, and Supporting Roles was integrated into the model.Furthermore, artificial intelligence was anchored in the dimensions as a complementary perspective and role-taking institution.The result is an updated matrix, offering an up-to-date and adaptable tool for managing complex environments.Based on these changes, the institutional role model can continue to create a cooperative environment in complex digital projects in the future and thus realize the added value of cooperation as a steering instrument.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".