Unfulfilled farmer expectations: the case of the Insect Resistant Maize for Africa (IRMA) project in Kenya
Bibliographic record
Abstract
Maize is the most important staple food in Kenya; any reduction in production and yield therefore often becomes a national food security concern. To address the challenge posed by the maize stem borer, the Insect Resistant Maize for Africa (IRMA) agricultural biotechnology public-private partnership (PPP) project was launched in 1999. There were, however, pre-existing concerns regarding the use of genetic engineering in crop production and skepticism about private sector involvement. The purpose of this case study was to understand the role of trust in the IRMA partnership by identifying the challenges to, and practices for, building trust in the project. Data were collected by conducting face-to-face, semi-structured interviews; reviewing publicly available project documents; and direct observations. The data were analyzed to generate recurring and emergent themes on how trust is understood and built among the partners in the IRMA project and between the project and the community. Clear and continued communication with stakeholders is of paramount importance to building trust, especially regarding competition among partners about project management positions; a lack of clarity on ownership of intellectual property rights (IPRs); and the influence of anti-genetic modification (GM) organizations. Awareness creation about IRMA’s anticipated products raised the end users’ expectations, which were unfulfilled due to failure to deliver Bacillus thuringiensis (Bt)-based products, thereby leading to diminished trust between the project and the community. Four key issues have been identified from the results of the study. First, the inability to deliver the intended products to the end user diminished stakeholders’ trust and interest in the project. Second, full and honest disclosure of information by partners when entering into project agreements is crucial to ensuring progress in a project. Third, engaging stakeholders and creating awareness immediately at the project’s inception contributes to trust building. Fourth, public sector goodwill combined with private sector technology and skills are necessary for a successful partnership. These findings may serve as a useful guide for building and fostering trust among partners in other agbiotech PPPs in sub-Saharan Africa.
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 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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.035 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".