The Adoption Drivers of New Technology: The Case of Genetically Modified Crop Adoption by French Farmers
Bibliographic record
Abstract
This work is the first study of adoption of GM crops by French farmers. The GM crop technology case was a fertile field for the study of technology adoption but existing literature mainly focuses on studies of these issues in USA, Africa and Asia. In France, main production area of maize in Europe, the agricultural sector is very atypical compared to the agricultural sector investigated in the existing literature on the GMO adoption. By a study of the GM crop adoption in the French context and within the main production area, the south-west region of France, this study reveals 4 determinant factors that shape the choice of GM or NGM maize cropping. Some of the factors pointed by this study are in line with the existing literature on technology adoption and output uncertainty and input uncertainty (uncertainty on the expected marginal benefits and costs to sustain the new technology). Other factors identified under the atypical French agriculture sector distinctive features are particularly novel. These new factors are linked to the implementation of the UE regulation on GM/NGM coexistence in the context of small fragmented farms (need of coordination between farmers to prevent GM dispersal) and importance of the market outlets proximity to federate farmers. These novel results pinpoint that in the French context there is a need of both vertical coordination (farmers with the operators downstream the supply chain as the outlet industrial) and horizontal coordination (among farmers in neighbourhood situation) to foster the technology adoption in such industry context.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".