Of Course a Land Use Regulation that Fails to Substantially Advance Legitimate State Interests Results in a Regulatory Taking
Bibliographic record
Abstract
A quarter of a century ago, the Supreme Court in Agins v. City of Tiburon announced that a land-use regulation violates the Fifth Amendment's takings clause if it fails to substantially advance legitimate state interests. Since 1980, the high court has reiterated this standard on nine occasions, and has found or upheld the finding of a regulatory taking in three of those cases. Nevertheless, regulatory advocates have fought a tireless campaign to discredit the substantial advancement takings standard and relegate it to a toothless due process inquiry. This article argues that the Supreme Court's substantial advancement test is an integral and coherent element of its regulatory takings jurisprudence. This standard is seen as a variation on one prong of the Penn Central balancing test announced by the Court just one term before Agins, with antecedents stretching back to the Court's first regulatory takings decision, Pennsylvania Coal v. Mahon.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".