Cultivating partnerships between CSOs and CGIAR centres: the case of a LI-BIRD Bioversity International International Partnership for in situ agricultural biodiversity conservation on farm in Nepal
Bibliographic record
Abstract
Background and objectiveThe CSO-NARS-CGIAR partnership linked LI-BIRD (Local Initiatives for Biodiversity, Research and Development) and Bioversity International in a global project with the aim of conserving agricultural biodiversity on-farm and improving the livelihoods of farming communities.The objective of the partnership was to combine Bioversity Internationals global expertise and knowledge with LI-BIRDs local knowledge and experience. Roles of the partnersLI-BIRD facilitates collaboration between the local farming communities and other project partners thus ensuring that local perspectives and knowledge are incorporated in conservation and development policies and programmes.It facilitates understanding of traditional practices and social dynamics in local farming communities.Bioversity International provides global expertise and facilitates cross-country sharing of project experiences.It contributes to enhancing the capacities of national partners and assists in synthesizing research findings to produce knowledge products and make them available for wider use.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.011 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 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".