Innovation Potentiality: Measuring Organisational Innovation Potential in the Canadian Public Sector
Bibliographic record
Abstract
Establishing a design and innovation (D&I) capability within a public sector organisation takes serious commitment and sponsorship from senior management. The leadership must commit to structural, political and cultural changes for the capability to succeed. When effective, design and innovation may cause disruption in current operational practices; become a catalyst for change; and challenge the powerful status quo. Such purposeful disruption can lead to significant progress and advancement for the organisation. However, these capabilities can often flounder due to insufficient preparation and lack of genuine dedication and endurance to overcome the obstacles that are inevitably encountered. This paper proposes a measurement model to evaluate the organisational readiness for D&I in public sector organisations. It is tested using the department of Innovation, Science and Economic Development (ISED) in Canada. The final score of 61.38% as the organisation’s innovation potential shows an organisation wanting to innovate but struggling with the appropriate implementation mechanisms and cultural norms. Through understanding the strength of factors contributing to the incorporation, practice and management of D&I, organisations can increase the probability of success and growth of these capabilities. Practical implications for organisational structures and operating models related to D&I are discussed.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".