The Enigma of Stigma: A New Environmental Contamination Challenge Facing Canada's Judiciary
Bibliographic record
Abstract
Would you buy previously contaminated property? The average citizen, given the choice between property with no history of contamination and one with prior environmental problems (even if they have been remediated), will instinctively choose the former over the latter. This is the phenomenon known as stigma. The recent case, Tridan Developments Ltd. v. Shell Canada Products Ltd. marks the first time that Canadian courts have squarely addressed the issue of stigma with the trial judge awarding damages for the diminution in property value over and above the costs of remediation for a contaminated property on the basis of stigma. The concept of stigma introduces a unique twist to the determination of damages because it is the product of market forces; it involves the subjective feelings of neither the plaintiff nor the defendant but is predicated on the perceptions of potential third party purchasers and contingent on the sale of "stigmatized" property.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.070 | 0.068 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.025 | 0.032 |
| Insufficient payload (model declined to judge) | 0.004 | 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".