Bibliographic record
Abstract
This paper introduces two novel applications of systemic design to facilitate a comparison of alternative methodologies that integrate systems thinking and design. In the first case study, systemic design helped the Procurement Department at the University of Toronto re-envision how public policy is implemented and how value is created in the broader university purchasing ecosystem. This resulted in an estimated $1.5 million in savings in the first year, and a rise in user retention rates from 40% to 99%. In the second case study, systemic design helped the clean energy and natural resources group within the Government of Alberta to design a more efficient and effective resource management system and shift the way that natural resource departments work together. This resulted in the formation of a standing systemic design team and contributed to the creation of an integrated resource management system. A comparative analysis of the two projects identifies a shared set of core principles for systemic design as well as areas of differentiation that reveal potential for learning across methodologies. Together, these case studies demonstrate the complementarity of systems thinking and design thinking, and show how they may be integrated to guide positive change within complex sociotechnical systems.
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".