“Protection to the Sulphur-Smoke Tort-feasors”: The Tragedy of Pollution in Sudbury, Ontario, the World's Nickel Capital, 1884–1927
Bibliographic record
Abstract
While there are many tales of mining companies polluting the Canadian communities in which they have operated, Sudbury's early history stands out. It is arguably the most extreme example of an industry dictating to government how the latter dealt with the local pollution problem – in this case, sulphur dioxide emissions. The capstone achievement was the creation of an extrajudicial solution to the problem that permanently suspended the legal rights of residents seeking redress for their grievances. Moreover, the Ontario government was duplicitous in this affair – namely, by zealously luring settlers to the region in an effort to develop farming there even though it was acutely aware of the local pollution problem. Finally, this story is truly tragic because the pollution need never have happened to the extent that it did. The provincial politicians knew full well that the means existed – within a short jaunt of Sudbury no less – to mitigate the problem, but the politicians refused to force the mining firms to adopt them. Retelling Sudbury's story thus highlights how the Ontario government's decision to grant the mining firms practical impunity to pollute the local environment – both human and non-human – was a matter of political choice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".