Bibliographic record
Abstract
The existence of contaminated sites in Canada has become a problem of nation-wide concern. Actions at civil and common law based on the traditional requirements of showing that property interests have been affected or personal injury has resulted are inadequate to address widespread harms arising from pollution. At the national level programs and policies have been developed to address clean-up of contaminated sites. At the provincial level legislation is being developed, directed at making persons responsible for the pollution they cause. Nonetheless, there are shortcomings under the present system in matters concerning victim redress and clean-up and restoration of contaminated sites. Victims are still struggling to obtain redress and compensation, especially in cases of defendant bankruptcy. It may be necessary as in the U.S. to create a Superfund to ensure compensation when there are orphan sites or when the defendant has become insolvent. There may be merit in establishing at the Federal level and in the other provinces a class action scheme along the lines of the Quebec model with an Assistance Fund to help litigants. In addition, there is a need to develop in legislation comprehensive requirements of clean-up and restoration of contaminated sites, that can be applied consistently nation-wide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.025 | 0.011 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".