Keeping the Actors in the Organic System Learning: The Role of Organic Farmers’ Experiments
Bibliographic record
Abstract
<p>The creative process that leads to farmers’ innovations is rarely studied or described precisely in agricultural sciences. For academic scientists, obvious limitations of farmers’ experiments are e.g. precision, reliability, robustness, accuracy, validity or the correct analysis of cause and effect. Nevertheless, we propose that ‘farmers’ experiments’ underpin innovations that keep organic farming locally tuned for sustainability and adaptable to changing economic, social and ecological conditions. We first researched the structure and role of farmers’ experiments by conducting semi-structured interviews of 47 organic farmers in Austria and 72 organic/agroecology farmers in Cuba in 2007 and 2008. Seventysix more structured interviews explored the topics and methods used by Austrian farmers that were ‘trying something’. Farmers engaged in activities that can be labelled as farmers experiments because these activities include considerable planning, manipulating variables, monitoring effects and communicating results. In Austria and Cuba 487 and 370 individual topics, respectively, were mentioned for experimenting by the respondents. These included topics like the introduction of new species or varieties, testing various ways of commercialization or the testing of alternative remedies. Two thirds (Austria) and one third (Cuba) of the farmers who experimented had an explicit mental or written plan before starting. In both countries, the majority of the farmers stated that they set up their experiments first on a small scale and expanded them if the outcome of the experiments was satisfactory. Repetitions were done by running experiments in subsequent years and the majority of the farmers monitored the experiments regularly. In both countries, many experiments were not discrete actions but nested in time and space. For further research on learning and innovation in organic farming we propose an explicit appreciation of farmers’ experiments, encouraging further in-depth research on the details of the farmers’ experimental process and encouraging the inclusion of farmers’ experiments in strategies for innovation in organic and non organic farming. Strategic research and innovation agendas for organic farming would benefit from including organic farmers as co-researchers in all steps of the research process in order to encourage co-learning between academic scientists and organic farmers.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".