A Systematic Mapping Review of Resilient Seed Systems
Bibliographic record
Abstract
Current formal agricultural seed systems are not resilient and have not necessarily ensured food diversity, nutrition and security. There is a need to identify local, traditional and Indigenous seed systems to acknowledge and understand the processes and outcomes which have led to their sustainability and resiliency. A systematic literature review was conducted with both academic, research for development, organization driven, and community generated literature with the aim of documenting and systematizing experiences of agricultural smallholders and Indigenous communities on the conservation of in-situ agrobiodiversity through informal and local seed systems for climate change adaptation and resilient livelihoods. This research identified organizational structures used in fostering in- situ conservation, their function as both technical and social innovations, several cross-cutting elements, and a key distinction between sustainability and resiliency as it relates to agriculture. This study presents and identifies a diversity of cases and experiences that can enable novel and local mechanisms to mobilize biodiversity conservation from local to international levels which can benefit Indigenous, family and small-scale agriculture in accessing diverse, good quality seed that can help address climate change and improve livelihoods while contributing to innovative use of neglected, orphan and underutilized species around the world. Practices to strengthen resilience at community and system levels are vital, as are new forms of collaboration with the private sector, academia, community-based organizations, and local and national governmental and non-governmental organizations. This information can serve to identify new themes for future research in this field internationally and in Ontario. Funding: SSHRC + Arrell Food Institute
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 | 0.022 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.044 | 0.035 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".