A Tale of Three Takings: Taking Analysis in Land Use Regulation in the United States, Australia and Canada
Bibliographic record
Abstract
The roots of the legal systems of the United States, Australia and Canada spring from a common English heritage in which protection of property is a prominent feature. Within these societies, when government expropriates private property, there is a presumption and, in some cases, a constitutional compulsion to compensate the owner. In the 1920s, the United States Supreme Court deviated from the principle in American and English law that compensation is required only when a government acquires a legal interest in or takes possession of property. In Pennsylvania Coal Co. v. Mahon, the Supreme Court found that regulating the use of property, in that case Pennsylvania Coal's mineral rights, may also require compensation if the regulation goes too far. Claims of regulatory taking did not become common, however, until the 1970s when land use and environmental regulation became pervasive. Property rights advocates, not only in the United States but also in Australia and Canada, sought more extensive protection when these regulations seriously devalued or limited the use of land. By the 1990s, cases in both Australia and Canada seemed to follow the lead of Mahon by requiring compensation for land use regulations that seriously devalued mineral rights. This paper surveys and compares the development of the concept of regulatory taking in the United States, Australia and Canada, and discusses each country's struggle to balance important public interests reflected in land use and environment regulation with protection of private property, and to develop a consistent theory of regulatory taking.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.020 | 0.025 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| 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".