Bibliographic record
Abstract
The apparent consensus among the proponents of proportionality, as Stephen Gardbaum has recently pointed out, is that the “triumphantly successful” constitutional law framework has few, if any, normative limits. Central to such broad understanding of proportionality is the assertion that almost any type of normative claim—whether instantiated in a legal order as an individual right or a public interest—can be fed into the algorithmic-like formula of proportionality with a view to obtain a clear and definitive answer as to which claim should take precedence. So understood, proportionality reasoning is an empty vessel, a doctrinal machine for processing normative judgements—something of a normative “omnivore”. The primary purpose of the present paper is to contest this received wisdom. It is argued that proportionality is a content-sensitive doctrinal framework that does have inherent limitations. In particular, it can only achieve its declared goals of enhancing legitimacy, rights priority, and rationality of judicial reasoning when applied to constitutional concerns conceived as negative injunctions—i.e. concerns that in the Kantian tradition operate as “paradigmatically enforceable claims to independence from others”. Conversely, when applied to positively conceived values—that is, values that entitle their holders to the provision of some services or goods—proportionality not merely remains neutral towards the foregoing triumvirate of goals, but actively undermines them. This paper explains how and why this is the case.
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 | 0.026 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.064 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".