Towards Systemic Theories of Change: High‐Leverage Strategies for Managing Wicked Problems
Bibliographic record
Abstract
Design and design management are increasingly called to respond to the world’s complex, dynamic problems. Yet, no standards or methodology exists to help designers understand, model, and design solutions for complex wicked problems. Program theory and social innovation promote the use of theory of change models to develop linear pathways of outcomes to show how a change initiative will have its desired effects. However, critics of these models accuse them of being simplistic and reductively linear. Systems thinking models use influence maps and causal loop diagrams to create maps of systems that show their behaviour in their full, dynamic complexity. However, these diagrams are sometimes complicated, overwhelming to read and therefore impractical. In this paper, we combine these tools with a novel technique from systemic design called “leverage analysis” to help identify crucial features of a complex problem and help designers develop practical theories of systemic change.
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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 | 0.030 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.014 | 0.027 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.008 |
| 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".