Glyphosate—A love story. Ordinary thoughtlessness and response‐ability in industrial farming
Bibliographic record
Abstract
Abstract More than 8.6 billion kilograms of glyphosate have been used worldwide since the 1970s. Herbicide tolerant crops became the lynchpin of the technological revolution for large‐scale farming first in the United States and Canada, and now in Europe. Zero‐till farming, as a production scheme and a world view, is based on simplifications promoted by a handful of transnational corporations with the complicity of politicians looking for easy solutions for problems, such as climate change, erosion and the hunger in the world. At the same time, the massive use of glyphosate is branded as an endocrine disrupter, causing cancer, male sterility and infertility. It interferes with soil bacteria and acts on the equilibrium of soil fungi. Glyphosate resistant crops connect farmers to far away consumers ingesting the food they grow together with the traces of chemicals. Farmers intra‐act with the myriads of life‐forms of the soil eco‐system. How do they perceive the life in the soil, when they spray chemicals? The article explores the political dimensions of the agency of both humans and non‐humans to understand the effects of the modernizing project of zero‐till, as well as to identify spaces and scales of possibility from where alternatives can emerge.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| 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".