From Intuition to Innovation: Harnessing Tacit Knowledge for Public Innovation A case study on the design of policy measures to address COVID-19 at the Public Health Agency of Canada
Bibliographic record
Abstract
This research explores how federal policy practitioners applied their tacit knowledge during the COVID-19 pandemic to design policy measures addressing this public health crisis. A qualitative case study involving semi-structured interviews with policy practitioners at the Public Health Agency of Canada (PHAC) examined their experiences navigating the demands and uncertainties of rapidly developing pandemic response policies. The findings revealed practitioners' heavy reliance on tacit knowledge - their accumulated experiences, intuition and contextual understanding - to fill information gaps and adapt to changing circumstances. However, they faced challenges articulating this personal, context-specific knowledge. The study highlights the role practitioners' tacit insights played in crisis policy development, demonstrating the importance of integrating both informational and inspirational design approaches. By institutionalizing practices that embrace tacit knowledge and diverse perspectives alongside empirical evidence, policy development processes can become more responsive and innovative, better equipping governments to address complex public challenges.
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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 | 0.040 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.041 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| 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".