Developing innovation capabilities through Programme Communities of Practice: Evidence from Wales
Bibliographic record
Abstract
This paper presents empirical evidence on how publicly funded programme communities of practice (CoPs) enhance learning and innovation activities at the organisational and inter-organisational level, which in turn develop organisation's innovation capabilities. It draws upon studies on two programme CoPs in Wales and presents eight case studies. Findings reveal, firstly, that CoP can promote the learning of new tools, knowledge, and technologies to encourage product and service innovation. Secondly, CoPs can promote innovative solutions to common challenges, such as implementing circular economy principles. Thirdly, CoP can facilitate collaborative inter-organisation innovation. Our paper answers the recent call for empirical research on the role of publicly funded collaborative projects that support business to innovate. It also expands the understanding of adopting CoP for management education. Practically the framework we developed can guide policy makers and practitioners on how universities can share risk, knowledge, and support organisations to develop their innovation capabilities and achieve sustainable development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".