Poisoning Regulation, Research, Health, and the Environment: The Glyphosate-Based Herbicides Case in Canada
Bibliographic record
Abstract
Despite discourse advocating pesticide reduction, there has been an exponential increase in pesticide use worldwide in the agricultural sector over the last 30 years. Glyphosate-Based Herbicides (GBHs) are the most widely used pesticides on the planet as well as in Canada, where a total of almost 470 million kilograms of declared "active" ingredient glyphosate was sold between 2007 and 2018. GBHs accounted for 58% of pesticides used in the agriculture sector in Canada in 2017. While the independent scientific literature on the harmful health and environmental impacts of pesticides such as GBHs is overwhelming, Canada has only banned 32 "active" pesticide ingredients out of 531 banned in 168 countries, and reapproved GBHs in 2017 until 2032. This article, based on interdisciplinary and intersectoral research, will analyze how as a result of the scientific and regulatory captures of relevant Canadian agencies by the pesticide industry, the Canadian regulation and scientific assessment of pesticides are deficient and lagging behind other countries, using the GBH case as a basis for analysis. It will show how, by embracing industry narratives and biased evidence, by being receptive to industry demands, and by opaque decision making and lack of transparency, Health Canada's Pest Management Regulatory Agency (PMRA) promotes commercial interests over the imperatives of public health and environmental protection.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".