Access to and Benefit Sharing of Plant Genetic Resources: Novel Field Experiences to Inform Policy
Bibliographic record
Abstract
A number of national and international policy processes are underway to allow for the development of sui generis systems to protect local natural and genetic resources and related knowledge about their management, use and maintenance. Despite agreements reached on paper at international and national levels, such as the Nagoya Protocol on access to genetic resources and the fair and equitable sharing of benefits derived from their use, and the International Treaty on Plant Genetic Resources for Food and Agriculture, progress in implementation has been slow and in many countries, painful. Promising examples from the field could stimulate policy debates and inspire implementation processes. Case studies from China, Cuba, Honduras, Jordan, Nepal, Peru and Syria offer examples of novel access and benefit sharing practices of local and indigenous farming communities. The examples are linked to new partnership configurations of multiple stakeholders interested in supporting these communities. The effective and fair implementation of mechanisms supported by appropriate policies and laws will ultimately be the most important assessment factor of the success of any formal access and benefit sharing regime.
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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 | 0.019 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.023 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".