The Practice Structured on the Discourse: Repertoires and Dominant Discourses in Brazilian Scientific Literature on Agrochemicals
Bibliographic record
Abstract
The present work aims to clarify the discursive practices of Brazilian scientific literature on chemical pesticides in order to understand, from a historical perspective, which repertoires are available to give meaning to the use of pesticides in Brazil. It further draws a picture of the positions taken in the technical area, from the creation of the “Agrochemicals Law” in 1989 until the present day. A total of 78 articles from ten journals were reviewed using the analysis of the discourses method as well as an overview of the database according to years, authors, and titles. The results show that scientific production is concentered in the south and southeast Brazilian institutions where the authors mostly come from. Their articles analyze presented repertoires that were categorized into three types: "required use of pesticides", "use of pesticides integrated with biological control”, and "no use of pesticides". The results suggest that there is no consensus in the discursive practices of Brazilian scientific literature on agrochemicals. It was concluded that there is in literature the suggestion that integrated pest control could improve the quality of production and lessen impacts on health and the environment. The dominant discourse is still linked to the paradigm that in order to have a high level of food production it is still necessary to use pesticides.
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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 | 0.031 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.012 | 0.035 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".