Comparison of Governance Policies for Agroforestry Initiatives: Lessons Learned from France and Quebec
Bibliographic record
Abstract
This study explores the fundamental components and specificities of agroforestry policies in France and Quebec, with a particular focus on the regions of Brittany and Montérégie. It uses a mixed-methods approach, combining an in-depth literature review and 14 semi-structured interviews with various stakeholders, including research institutions, agricultural advisory organizations, independent experts, and regional public agencies engaged in agroforestry and environmental initiatives. The collected data were qualitatively analyzed using word frequency and co-occurrence techniques, based on Elinor Ostrom’s Institutional Analysis and Development (IAD) framework. The results reveal that in France, agroforestry benefits from a well-structured policy environment, centered on the Common Agricultural Policy (CAP) and the Agroforestry Development Plan (PDA). The Breizh Bocage initiative is making a positive contribution to this, with more than 5000 km of hedges planted thanks to its localized governance model and direct community funding. In Quebec, agroforestry is also supported by various policies and programs such as Prime-Vert, with more than 2370 hedge planting projects completed. Despite its strengths, the French case, particularly the Breizh Bocage program, is limited by cumbersome administrative procedures. In both contexts, stakeholders emphasize the need to improve the transparency and efficiency of the program by simplifying administrative processes and harmonizing financial support mechanisms.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".