Design Thinking in the Canadian Public Sector: an exploration of suitability for problem solving in policy development through the use of an interdisciplinary design thinking workshop.
Bibliographic record
Abstract
The Canadian federal government is constantly in search of processes and approaches to address mounting complex challenges in the public sector.Design thinking is an approach that has been successfully implemented in the Australian government and has been used lately in many Canadian government departments to facilitate innovation.Yet, academic literature about design thinking in the Canadian context of policy development, specifically policy and decision making, is sparse.The research conducted in this thesis furthers new thinking in this area.A design thinking workshop based on a collaborative interdisciplinary approach was used to gain insights into the potential barriers and benefits of design thinking in the Canadian public sector.Experienced designers and public sector experts were invited to participate in a policy design challenge, at Carleton University, to discuss food insecurity in Inuit Nunangat, an example of a real and complex wicked problem facing the Canadian government.The findings revealed potential alternative solutions requiring increased interdisciplinary collaboration between and among government policy makers and design practitioners, incorporating experienced designers into the policy process with policy experts, and using results to bolster academic literature.
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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 | 0.035 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.039 | 0.031 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".