Legitimizing unsustainable practices: The institutional logics of pro‐pesticide organizations
Bibliographic record
Abstract
Abstract The objective of this study is to analyze the institutional logics underlying pesticide use and the resistance displayed by organizations in this sector against social pressures to reduce the use of these substances. This in‐depth study of a public hearing on pesticides set up by the National Assembly of Quebec (Canada) in 2019 shows the often very strong positions held by the relevant stakeholders and how they legitimize their positions. The qualitative content analysis of 77 briefs and 30 testimonies highlights five main institutional logics that contribute to the institutionalization of pesticide use despite the strong opposition it generates: the economic and strategic logic, the regulatory and administrative logic, the tailored advice and support logic, the research and innovation logic, and the traditional, rural and pragmatic logic. These logics show how the objectives, belief systems, and practices shared by pro‐pesticide organizations can hold sway, including over public bodies that are a priori independent but tend to play a buffering and facilitating role in the use of these controversial products. This article contributes to the literature on institutional logics and corporate sustainability by showing how some of these logics can contribute to the continuation of unsustainable practices over time. The article also contributes to the often highly technical literature on the use and impacts of pesticides by proposing an institutional approach that provides an overall picture of the positions of several interdependent organizations and how their underlying belief systems influence practices. Practical implications and avenues for future research are also discussed.
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 | 0.027 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.080 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".