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Record W2587895189 · doi:10.5539/sar.v6n2p26

Realizing Farmers’ Rights through Community Seed Banks in Uganda: Experiences and Policy Issues

2017· article· en· W2587895189 on OpenAlexvenueno aff
Gloria Otieno, Catherine Kiwuka, J W Mulumba

Bibliographic record

VenueSustainable Agriculture Research · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsLegislatureLegislationBusinessGovernment (linguistics)Quality (philosophy)Political sciencePublic relations

Abstract

fetched live from OpenAlex

The paper interrogates the role of community seed banks (CSBs) and related initiatives in the realization of farmers’ rights in Uganda and the policy and legislative space for the functioning of CSBs. The study finds that although community seed banks are a relatively new phenomenon in Uganda, there have been community based seed banking initiatives that have been instrumental in the realization of farmers’ rights to save and exchange seed and information; and especially providing a wide range of diversity of seed to farmers and improving access to good quality seed. Through partnerships with local Non-Governmental Organizations (NGOs), research and government institutions, CSBs have received technical and financial support for conservation and seed production activities, thus enabling them to participate in seed value-chains through production of quality declared seed (QDS) and participate in decision making. Although the policy and legal environment for the functioning of CSBs is not well defined, various pieces of draft legislation provide positively for ways through which CSBs can be recognized and supported for the benefit of farmers. The study recommends that CSBs activities should be rolled-out to other parts of the country through a government financing mechanism that is suggested in the draft national policy on plant genetic resources for food and agriculture. The development of a policy and legal environment that includes an act that has provisions for the recognition of CSBs and the protection of farmers’ rights is important. Secondary information, interviews with key informants and Focus Group discussions (FGDs) are the primary sources of data used.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0070.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.100
GPT teacher head0.408
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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