Knowing through doing: unleashing latent dynamic capabilities in the public sector
Bibliographic record
Abstract
Introduction Public sector managers are increasingly asked to do more with less. They are expected to find ways to improve organisational performance – even in times of decreasing financial resources, and often in the face of scarce human resources. Even if they know how to make improvements, they may not be able to implement the appropriate changes. A gap may exist between what is known and what is done – what Pfeffer and Sutton (2000) called ‘the knowing–doing gap’. The newer waves of knowledge translation research (e.g. Grimshaw et al . 2005) frame the gap as a disconnection between knowledge acquisition and the use or implementation of that knowledge. Similarly, a recent systematic review of diffusion of innovation (Greenhalgh et al . 2004) refers to the lags or gaps between knowledge, action and innovation as indication of oversimplification of diffusion models combined with a lack of robust evidence concerning how diffusion occurs and indicators of when and how it is successful. In addressing the knowing–doing gap within the context of improving performance in a public sector organisation, a shortage of management or organisation-based knowledge can create a gap in knowledge acquisition. The assumption that public sector managers should be able to understand what needs to be done to achieve organisational goals and activities presumes their ability to develop and use managerial strategies towards these ends. Accordingly, strategy in the public sector has become an increasingly relevant research topic (Llewellyn and Tappin 2003).
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".