Bibliographic record
Abstract
This study explored how design thinking plays a role in innovation centres and hubs around Canada, with a focus on social innovation.The work involved exploring how and why the design thinking approach is used in innovation centres and hubs.The research incorporated the expert interview method, which addressed the perspective of senior experts of innovation centres and hubs on the research question: What is the role of design thinking in innovation centres and hubs?A thematic analysis with deductive and inductive approaches was completed to examine the data collected from the interview sessions.The results of this thesis provide beneficial insights to better understand the systematic approach of innovation centres and hubs.These organizations can benefit from a comprehensive understanding of the work processes of the innovation ecosystem across Canada and systematic strategies of other centres and hubs' approaches.On the other hand, this thesis helps the design community better comprehend the impact of the design thinking approach in the absence of professional designers.The researcher has presented a comprehensive conclusion of the research project and explained the study's key takeaways, considering the limitations and possible opportunities for this field of research.Finally, the researcher has provided recommendations for future research that can particularly help researchers in design thinking, social innovation, and innovation centres and hubs to determine future topics.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 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".