Bibliographic record
Abstract
Emergency powers are as old as constitutionalism, reflecting fundamental tensions in the rule of law that pervade many aspects of political life. Hence, exploring philosophical and institutional aspects of emergencies can lead to broader conclusions about law and individual power. By engaging not just emergency powers but aspects of administrative law with a similar structure, David Dyzenhaus concludes that all branches of government ought to rule in a ‘spirit of legality’ according to principles underlying the rule of law. This aspirational conception of the rule of law raises important questions about the relationship between empirical and normative enquiry. Related questions arise in Oren Gross and Fionnuala Ní Aoláin's new book. These authors argue that existing emergency law rests on an empirical mistake with serious normative consequences. If emergencies lack discrete beginnings and endings, which institutional arrangements minimize these consequences? Both books leave critical questions in their wake, brilliantly exploring – without settling – the question of extra-legal action. The complex relationship of is and ought remains: What is the place of empirical facts of political life in normative enquiry? And what role do normative considerations play in establishing empirical conditions?
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.062 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".