Labour and the Environment: Five Stories from New Brunswick Since the 1970s
Bibliographic record
Abstract
A clip circulating on the Internet of the announcement of new investment in Irving Pulp & Paper shows Jerry Dias, the national president of Unifor, at the mill in Saint John, New Brunswick thanking Jim Irving, President of J.D. Irving Ltd., and the Irving workers for a job well done in getting a new forestry plan in NB.1 The event, with Premier David Alward in attendance, took place just after the New Brunswick government’s 12 March 2014 announcement of the plan. The latter, which increases the cut on the province’s Crown lands, has been emphatically denounced by environmentalists. This alliance between labour and industry, by ignoring environmentalists’ concerns, is an aberration. In the past 40-plus years, and at present in other sectors in New Brunswick, labour has a history of alliance-building and significant cooperation with environmentalists. This is not just an issue in New Brunswick. Throughout North America and much of the world, the question of whether the labour and environmental movements can work together has been a central one.2 Government and industry would have us believe that there is an inherent conflict between jobs and the environment. Yet many have argued that juxtaposing “jobs versus the environment” is a false choice and that the environmental and labour movements can work in alliances to build a more sustainable world. Indeed, my
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.042 | 0.017 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".