Leveraging User-Centred Design Methods in Canadian Public Service Innovation Labs
Bibliographic record
Abstract
Innovation labs have emerged as a promising means to improve the capacity of public sector organisations to address complex challenges through creativity, experimentation, and multi-disciplinary collaboration. Yet, many Canadian public service innovation labs struggle to sustain momentum, generate measurable outcomes, or scale successful innovations. This thesis investigates the role of User-Centred Design (UCD) methodologies in enhancing the effectiveness of public sector innovation labs. Drawing on design science research methodology and case studies including federal, provincial, and international examples, this research proposes a framework to support innovation lab practice. The framework includes three interrelated artefacts: a typology to classify innovation labs based on their funding, focus, approach to collaboration, and governance mandate; a structured process model for lab setup, system definition, co-design and scaling of innovations; and a playbook to guide individual innovation projects to ensure consistent results, capacity building, and knowledge transfer. These artefacts were iteratively developed and refined through five design iterations and validated through stakeholder engagement and an expert panel review. Findings indicate that a systematic approach to incorporating UCD methods such as co-design, rapid prototyping, and iterative evaluation improve problem definition, team cohesion, and the likelihood of generating scalable public sector innovations. However, the research also reveals that organisational maturity, leadership support, and cross-sector collaboration are critical enablers of success. By aligning innovation lab practices with the principles of UCD and adapting them to the operational realities of the public sector, this thesis offers a blueprint for transforming public service innovation.
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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 | 0.153 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| 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".