Building trust in biotechnology crops in light of the Arab Spring: a case study of Bt maize in Egypt
Bibliographic record
Abstract
The case of Bacillus thuringiensis (Bt) maize in Egypt presents a unique perspective on the role of trust in agricultural biotechnology (agbiotech) public-private partnerships (PPPs). This is especially relevant given the recent pro-democracy uprisings that spread throughout the Arab world that have significantly impacted the current political climate and status of both the public and private sector, and especially public-private collaborative initiatives. This case study aims to shed light on various trust-building practices adopted, and trust-related challenges faced, in the Bt maize project in Egypt. We reviewed published materials on Bt maize in Egypt and collected data through direct observations and semi-structured, face-to-face interviews with stakeholders of the Bt maize project in Egypt. Data from the interviews were analyzed based on emergent themes to create a comprehensive narrative on how trust is understood and built among the partners and with the community. We have distilled five key lessons from this case study. First, it is important to have transparent interactions and clearly defined project priorities, roles and responsibilities among core partners. Second, partners need to engage farmers by using proven-effective, hands-on approaches as a means for farmers to build trust in the technology. Third, positive interactions with the technology are important; increased yields and secure income attributable to the seed will facilitate trust. Fourth, there is a need for improved communication strategies and appropriate media response to obviate unwarranted public perceptions of the project. Finally, the political context cannot be ignored; there is a need to establish trust in both the public and private sector as a means to secure the future of agbiotech PPPs in Egypt. Most important to the case of Egypt is the effect of the current political climate on project success. There is reason to believe that the current political situation will dictate the ability of public institutions and private corporations to engage in trusting 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".