Bibliographic record
Abstract
One of the main criticisms directed against theories proposed by authors such as Alexy and Barak — theories grouped under the term proportionality as — has been the incommensurability objection. That is, the objection that it is not appropriate to compare the ways in which human rights and public interests are affected in judicial review cases using a common measure. In this article, I analyze as maximization and the incommensuability objection using Ruth Chang's theory. The goal is to show that Chang's ideas — particularly her idea of — provides an alternative way of challenging balancing that has similar results than the incommensurability objection, but without accepting the premise that there is no common measure under which the gains and losses of human rights and public interests can be compared. I also suggest that Chang's parity has two advantages over incommensurability as grounds for challenging proportionality: First, parity's respect for human intuition; and second, parity's flexibility.
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 | 0.021 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".