In Defence of Two-Step Balancing and Proportionality in Rights Adjudication
Bibliographic record
Abstract
Two-step proportionality-balancing [TSPB] has become the standard method for human and constitutional rights decision-making. The first step consists in determining whether a rights-provision has been infringed/limited; if the answer to that first question is positive, the second step consists in determining whether the infringement/limit is reasonable or justified according to a proportionality analysis. TSPB has regularly been the target of some criticism. Critiques have argued that both its ‘two-step’ and ‘proportionality’ elements distort reality by promoting a false picture of rights and constitutional decision-making. This would cause negative moral consequences. This article seeks to defend TSPB against these criticisms and to depict it in a more appropriate and favourable light. First, it is argued that both aspects of TSPB do not have the dire moral consequences that opponents suggest they have. Second, it is argued that TSPB, deploying notions such as burdens, presumptions andprima facie/defeasible propositions, constitutes a valuable framework for public argumentation and authoritative decision-making.
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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 | 0.070 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".