Conditional knowledge and debugging strategies help overcome creative endeavours’ costs: Can we use successful innovators’ tactics for innovation education?
Bibliographic record
Abstract
Given that society depends on a steady supply of innovators to overcome challenges, innovation education efforts must be amplified to promote innovation skills among students. A key aspect of promoting innovation in context is understanding how and why innovators are motivated to innovate. However, even in the limited emerging literature on motivating innovation, there is a paucity of information on established innovators’ motivations for innovating. This two-phase, primarily qualitative study addresses this knowledge gap by investigating the strategies that innovators used to make their creative endeavours more likely, combining semi-structured interviews and a survey of Canadian innovators. Interviews were conducted with a diverse, multidisciplinary sample of 30 Canadian innovators which informed the development of the survey administered to a larger sample of 500 Canadian innovators. Participants reported costs of innovating and from their perspectives offered advice for aspiring innovators. Specifically, innovators detailed advice and approaches for aspiring innovators to maximize expectancies, maximize values, and proactively mitigate costs. This study calls for innovation education to focus on building capacity for developing innovators based on the strategies of successful innovators. These promotive and mitigating strategies are useful guides for educators, leaders, and of course for innovators themselves. Study results direct attention to approaches that should be integrated into programs designed to promote innovation across disciplines, organizations, and learning contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".