Exploring and Exploiting the Dynamics of Networks in Complex Applied Research Projects: A Reflection on Learning in Action
Bibliographic record
Abstract
Abstract Since 1984, the European Union (EU) has supported research and development activities covering almost all scientific disciplines through a series of multi‐annual Framework Programmes. The current programme is Horizon 2020. Common across the key indicators of research project performance have been actions by companies, including introduce and test innovations new to the company or the market. Initiatives to achieve these objectives require researchers to generate transdisciplinary knowledge in partnership with practitioners as co‐researchers. This paper reflects on the authors’ experience of engaging in five EU‐funded complex applied research projects over 20 years. The paper locates the process of the five projects in network action learning and Mode 2 knowledge production. It offers a theoretical framework expressed in three hypotheses to guide those who design and implement projects, those who approve and provide funding, and those who exploit and build upon the resulting research.
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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 | 0.035 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.041 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".