Analysis of Cross-Sectoral Collaboration. Deliverable 2.3 of the ShapingBio project
Bibliographic record
Abstract
This publication is a deliverable of the project ShapingBio, a HorizonEurope funded Coordination and Support Action. Its aims are to gain a deeperunderstanding of the EU bioeconomy innovation eco-system and to derive recommendations on which measures should be taken, by EU, Member States, and others to strengthen bioeconomy innovation systems. This report D2.3 offers a comprehensive overview of the primary obstacles and identifies supportive measures that influence cross-sectoral collaboration (CSC) throughout the EU. The focus of the analysis was on existing collaborative structures across Europe, representing the main bio-based value chains: agriculture, forestry, blue bioeconomy and food sector. The analysis reveals that numerous common factors hinder cross-sectoral collaboration across the EU, such as a lack of financing and missing culture of CSC. Governance and policy supporting the bioeconomy are important for the initiation and development of CSC. Strategies, and initiatives, ranging from regional tonational to EU-wide, are necessary to collectively contribute to building robust frameworks, which encourage collaboration between different sectors and, therefore, boost knowledge exchange, resource optimization, and innovation. However, providing general support for the bioeconomy will not be sufficient to create efficient and effective CSC. Insufficient and incompatible infrastructure with strong regular disparities is also a major hurdle together with inadequate investments and lack of alignment of financing for CSC projects, which are associated with higher risk and uncertainties compared to non-CSC projects and need therefore dedicated financial instruments. Social capital additionally is identified as crucial in CSC, as trust and building effective communication channels among stakeholders from different sectors, is a key factor in establishing effective collaborative activities and networks. Furthermore, the landscape of the bioeconomy varies significantly across Europe, which implies that the key challenges and the specific sector profiles – such as the types of biomasses available, existing value chains, and policy developments – differ extensively across various member states. This diversity causes a variety of different gaps and challenges of CSC for involved stakeholder groups and necessitates tailored actions to overcome theexisting barriers in the involved bioeconomy sectors.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".