Glyphosate-based forest management in New Brunswick: regulatory context, socio-environmental health, and industry discourse
Bibliographic record
Abstract
Forestry is a major economic sector in New Brunswick. For many years, forestry management practices have been subject to controversies, and different positions persist. Aerial spraying of glyphosate as part of forest management practices emerged over the last decade as a contentious issue in the province. In this chapter, we present an analysis of the narrative created by the forest industry in support of glyphosate use. We argue that premier Blain Higgs’ policy record is coherent with this narrative that reconciles the oft-heard opposition between “the environment” and “the economy” through ever-more advanced technologies such as glyphosate. The first section provides background information on glyphosate use in forest management in NB. In the second section, we discuss the regulatory context of glyphosate use. The third and fourth sections present a structural discourse analysis of a small corpus of industry discourse, mainly focused on the presentation by the company JD Irving at the 2021 public hearings held by the Standing Committee on Climate Change and Environmenta Stewardship. We conclude by looking back at the Higgs government’s (in)action following the public hearings that hints at a causal link between industry discourse and government policy.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".