Innovation brokers in High Nature Value farming areas: a strategic approach to engage effective socioeconomic and agroecological dynamics
Bibliographic record
Abstract
High Nature Value (HNV) farmlands currently retain most of the biodiversity associated with agricultural landscapes in Europe. In a time of globalized food systems, the social-ecological conditions to maintain these low-intensity and thus less productive HNV farming systems are difficult to meet. Halting the loss of HNV farmland requires fostering the socioeconomic viability of HNV farming systems that is compatible with social, cultural, and ecological values. Pursuing such viability calls for tailored actions to steer the development of HNV farming systems based on the strength of their local assets. Such a transformational learning process involves changing the territorial dynamic towards better integration of biodiversity at several levels of management (from farm to territorial level). Based on the description and analysis of ten HNV territories distributed across Europe, we explore how HNV innovation brokers can strategically engage with local actors to preserve the environmental characteristics of HNV farmland areas while improving their socioeconomic viability. The aim of this research is to improve the understanding of the range of approaches and strategies of innovation brokers to meet the challenges of HNV farmland conservation. The study analyzes the different innovation processes that took place in each area, concentrating on the engagement phase. Our results demonstrate that HNV farming situations across Europe are quite diverse from an agroecological and socioeconomic point of view. There are distinct conservation challenges and associated risks for each HNV farming context. The need for a strategic approach to HNV conservation at landscape–territory level is discussed. The key role of innovation brokers is highlighted, together with the need for a strategic approach to innovation brokerage, which is explicit in relation to territorial needs and the changes required. We demonstrate the importance of the landscape–territorial vision as an entry point for shaping HNV farming systems towards socially desirable scenarios.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".