National and Local Definitions of an Environmental Nuisance: Water Pollution and River Decontamination in Six Urban Areas of Quebec, 1945–1980
Bibliographic record
Abstract
When it created the Water Purification Board in 1961, the Quebec government intended to proceed with a major reorganization of the municipal wastewater treatment and drinking water systems throughout the province. In the following decades, the Department of Municipal Affairs and Environment developed a series of programs and policies for the treatment of wastewater. If water pollution then appeared as a national problem and the subject of a consensual definition, neighbouring communities were facing specific problems that government policies tended to obscure. Our analysis of six municipalities (Drummondville, Sherbrooke, Saint-Hyacinthe, Granby, Trois-Rivières, and Shawinigan) located in three river basins (Saint-François, Yamaska, and Saint-Maurice), each with its own topography and hydrology, population, and industrial growth, and political and cultural history, reveals precisely how communities articulated their different understandings of pollution problems, as well as their distinct definitions of nuisance and means of coping with pollution. By identifying, at the local level, multiple representations of pollution phenomena and practices put forward to decontaminate water, we shed light on the difficulties surrounding the implementation of water treatment infrastructure in municipalities across Quebec between 1945 and 1980.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".