What is trust?: perspectives from farmers and other experts in the field of agriculture in Africa
Bibliographic record
Abstract
Agricultural biotechnology public-private partnerships (PPPs) have been recognized as necessary for improving agricultural productivity and increasing food production in sub-Saharan Africa. However, there are issues of public trust uniquely associated with PPPs involved in the development of genetically modified (GM) crops. Insight into how trust is understood by agbiotech stakeholders is needed to be able to promote and improve trust among actors comprising agbiotech PPPs. This study aimed to explore how stakeholders from the agricultural sector in sub-Saharan Africa understood the concept of trust in general as well as in the context of agbiotech PPPs. Our data collection relied on sixty-one semi-structured, face-to-face interviews conducted with agbiotech stakeholders as part of a larger study investigating the role of trust in eight agbiotech projects across Africa. Interview transcripts were analyzed to create a narrative on how trust is understood by the study’s participants. Responses to the question “what is trust?” were diverse. However, across interviewees’ responses we identified six themes. In order to build and foster trust in a partnership, partners reported that one must practice integrity and honesty; deliver results in an accountable manner; be capable and competent; share the same objectives and interests; be transparent about actions and intentions through clear communication; and target services toward the interests of the public. Participants reported that trust is either a very important factor or the most important factor in the making or breaking of success in agbiotech PPPs. The six themes that emerged from the interview data form a concept of trust. We thereby propose the following definition of trust in the context of agricultural biotechnology: an expectation held by an individual that the performance and behaviour of another will be supported by tangible results; facilitated by competency and transparency; grounded in a shared vision; and guided by integrity and an interest for the common good. This definition sheds light on important elements that agbiotech stakeholders believe should be present for trust to exist among members of agbiotech PPPs, for whom this definition can serve as a guide for building more effective partnerships.
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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 | 0.032 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.036 | 0.024 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.012 |
| 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".