Bibliographic record
Abstract
What: This paper reports on the results of participative action research by multiple teams of participants who played various roles and fostered the evolution of an integrated research and business simulation environment by sharing data, making decisions visible and discussing solutions in both a competitive and a collaborative environment. Why: Collaborative Networked Learning is needed for the training of effective management and operation of global corporate entities and in understanding the value of integrating information systems between organizations that collaborate and compete with each other in different times and markets. This is necessary since competition, in business today, is between supply chains of competing collectives of organizations, each seeking a larger market share and bigger profits and where changes in partnerships come at an ever increasing pace. Who: Managers, students, tutors & administrators of classroom, online courses, and boardroom based professional development programs. When: During the period July 2003-December 2005 using the simulator located at www.sccori.com Where: In online courses, residential programs, in bricks and mortar classrooms and in the boardrooms of major corporations. How: Using an internet browser-based online business simulator and internet communications tools allowing participants to play the roles of Retailer, Wholesaler, Distributor and Manufacturer in a number of business simulations with variable parameters. Participants experience simulations and learn by doing, build problem solving skills, develop strategies, plan, negotiate, share, build trust and implement solutions. Results: The networked management learning business simulator improves the systems dynamics models from MIT and the Systems Dynamics Society by at least two orders of magnitude. Results indicate that participants move from an individualistic competitive stance to a collaborative team-based solutions focus to threats and problems faced by their supply chain during increasingly challenging business simulations.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".