Building effective agbiotech partnerships founded on trust: a summary of the challenges and practices in sub-Saharan Africa
Bibliographic record
Abstract
The potential for improving the efficiency and success of partnerships in agricultural biotechnology is contingent on the presence of trust. As Stephen Covey (2006) asserts in his book The Speed of Trust, trust is the basis of the new global economy and is an essential element of any successful organization [1]. The presence of trust is particularly important in public-private partnerships (PPPs), in which partners with varied interests, goals, and operating principles embark on complex tasks within innovative ventures. Even more crucial is the role of trust in the success of agbiotech initiatives led by PPPs. This is cited throughout the literature on trust and has been confirmed by numerous agricultural stakeholders who participated in our case studies of agbiotech PPPs in Africa [2-9]. Stakeholders linked project successes with the establishment and maintenance of trust throughout the duration of their respective partnerships. When trust was broken or nonexistent in the partnerships, stakeholders reported an evident negative impact on the partnerships. In several cases, stakeholders cited instances in which the erosion of trust led to severed ties among stakeholders of the project or to slow progression of certain phases of the projects, such as product development or biosafety approval.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".