Toward understanding the governance of varietal and genetic diversity
Bibliographic record
Abstract
Varietal and genetic diversity sustain modern agriculture and is provided by breeding systems.Failures in these systems may cause insufficient responses to plant diseases, which threatens food security.To avoid these failures, an understanding of the governance challenges in providing varietal and genetic diversity is required.Previous studies acknowledge the complexity of seed breeding, framing the discussion in terms of rivalry and excludability.We consider breeding systems as social-ecological systems that focus on activities that generate varietal and genetic diversity and their adaptive ability.We use an inductive approach based on qualitative methods combined with the social-ecological system framework (SESF) to depict how highly context-dependent German winter wheat breeding, multiplication, and farming activities are.Our results show that the challenges for governance lie in providing credible and symmetric information on variety performance to all actors.This is the means to steer actors into collective action by subcontracting, buying, or saving seed.Based on our application of the SESF to the German wheat breeding system, we propose to develop a more general, sectoral SESF for the sustainable governance of plant breeding by offering an adaptable template for analyses of seed systems in other contexts.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".