Collaborative Systems Thinking Culture: A Path to Success for Complex Projects
Bibliographic record
Abstract
Abstract The world is filled with hard and complex problems, oftentimes requiring involved solutions. In large organizations attempting to solve these types of problems, a mindset shift and key candidate methodologies centered on collaborative systems thinking culture (CSTC) can assist significantly. The paper explores the state of the practice, change involved with implementing systems thinking, impacts of a collaborative approach within an organization, as well as the seven phases that a reader can introduce into their organization to realize some of the benefits. The same approach was used to create this paper under collective authorship from Cohort 6 of the INCOSE Technical Leadership Institute (TLI); an international group of individuals collaborating exclusively through virtual platforms. From writing papers to executing large technical programs, the CSTC approach will prepare technical teams for tackling challenging problems in an inclusive way with the intent to finish projects on time while also cultivating healthy systems engineering habits and practices. This lessens the reliance on corporate engineering procedures to drive collaborative behavior by fiat. Finally, blending CSTC into the fabric and culture of an organization is emphasized as being needed for the full benefit. That benefit includes saving programs by moving to a CSTC.
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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 | 0.024 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.026 | 0.007 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".