120 Reports on the Context Analysis (including inventory of 120 context profiles)
Bibliographic record
Abstract
This document is part of T3.2 (Conducting Context Analysis improving transferability & spreading of best practices) within the Grazing4Agroecology project. It presents the outcomes of the Context Analysis, which includes an inventory of 121 context profiles showcasing innovative practices implemented by grazing-based farming systems in an agroecological perspective. Building on the methodological framework outlined in Deliverable D3.8, which provides guidelines for conducting a context analysis, this task was carried out between the project months 3 and 32. Context Analysis is a process that combines best practices with scientific knowledge to enhance thetransferability and adoption of innovations, thus providing a tool making the dissemination and application of innovations more efficient. An innovation will only be an innovation and have a positive impact where it fits. We believe that innovations and new ideas should be evaluated to narrow to environments in which they might be applied. This would greatly increase the effectiveness and efficiency of the process. The Context Analysis is meant to provide a link between the innovator and the wider farming community. In order to make things work there should be a certain degree of identity between the sender and potential receivers9. In this sense, the sender is the innovation in the context of origin and the receiving end is the environment which is supposed to be improved through implementing this innovation. The degree of identity is what must be assessed to allow for a successful transfer.The Context Analysis serves as a bridge between local innovation and broader application. By combining practice-based knowledge with scientific evaluation, the aim is to identify and refine innovations that can be adapted and transferred across different European regions. This approach supports more effective dissemination and adoption by highlighting the conditions under which specific practices are most likely to succeed. The goal is to transform locally grounded insights into transferable knowledge, offering a valuable tool for farmers and other stakeholder categories seeking to apply different innovations in varied contexts across Europe.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.017 |
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".