Designing Choices: Capital Design & Design Education | Jordan Ostapchuk | SystemsThinking Ontario | 2025-04-10
Bibliographic record
Abstract
"Designing Choices: Capital Design & Design Education", the 130th meeting of Systems Thinking Ontario, was announced at https://wiki.st-on.org/2025-04-10 “Are you making choices or decisions?” A key tension and theme that Jordan Ostapchuk has been investigating throughout his professional and academic pursuits. In this session, we’ll have a fire-side chat with Jordan to learn about his education journey from graduating from the SFI program at OCADU to completing his PhD at Institute of Design at IIT. We’ll learn about how Jordan weaves his professional experiences in management and infrastructure investment into his design scholarship. We'll also discuss the ways systems thinking shows up in 'Capital Design', which is the focus of Jordan's dissertation and his professional work. ## Discussant Jordan Ostapchuk advises institutional investors and capital allocators on strategic choice-making. He spent a decade at OMERS, a $130B+ pension plan, where he led strategy, innovation, and communication functions across Infrastructure, Pensions, and Real Estate. Most recently, he led OMERS Infrastructure's investment strategy function. He started his career as a Consultant at Deloitte. Jordan has a PhD from the Institute of Design at Illinois Institute of Technology on the role of design in institutional capital allocation, which created the field of Capital Design. Jordan also has an M.A. from the University of Southern California and an M.Des. in Strategic Foresight and Innovation from OCAD University. https://www.jordanostapchuk.com/
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.003 |
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; both teacher heads agree on what is shown here.
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".