From Humanist Design Perspectives to Human-Centered Design: A Canadian Public Service Organisation Co-Design Strategy
Bibliographic record
Abstract
This Master’s Degree Research-Creation project explores the development of a co-design strategy informed by humanist design principles and human-centered design approaches.Through its research-creation process and outcomes, this study aimed to humanise and democratise bureaucracy by integrating a broader range of approaches and drawing on Julien Hébert’s philosophy as a foundation for socially engaged design practice that fosters trust. In doing so, this research aims to investigate how such a co-design strategy can identify opportunities and challenges for embedding design in public organisations for the socio-cultural benefit of Canadian citizens and public servants alike. Utilising Metro North Health’s Co-Design Process, the project established a structured co-design lookbook for federal partners, stakeholders, and the Canadian public, ensuring adaptability for broader applications. A literature review examined mid-century humanist design perspectives through the work of Richard Buckminster Fuller, Victor Papanek, Jane Jacobs, Gui Bonsiepe, and Julien Hébert. These designers advocated for the socio-cultural significance of design and the integration of participatory methodologies. Case study research on contemporary human-centered design initiatives further demonstrated how co-design fosters systemic change and sustainable practices. Insights from this analysis guided the development of a strategy emphasising accessibility, empathy, and participation as core principles for integrating human-centered design into public service functions and multidisciplinary teams. The strategy was developed through a months-long co-design process, including three collaborative sessions with public servants. This iterative approach identified key challenges such as siloed operations and the need for design-driven methodologies in public service. The final design outputs—a logomark, landing page, lookbook, and systems map—were evaluated and refined based on participant feedback. Drawing from Julien Hébert’s humanist design philosophy, the proposed strategy offers an adaptable model applicable across various public service functions, reinforcing design’s role as a driver of positive social 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 | 0.108 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.040 | 0.060 |
| Scholarly communication | 0.030 | 0.011 |
| Open science | 0.007 | 0.027 |
| Research integrity | 0.009 | 0.010 |
| 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".