Bibliographic record
Abstract
Abstract Margit Cohn has written a terrific new book about the executive branch of state. Writing against the backdrop of a wave of executive aggrandizement in constitutional democracies around the world, Cohn sets herself the ambitious task of constructing a theoretical account of executive power. What emerges is a theory built around the twin themes of tension and legality. In most constitutional systems, she argues, the law provides broad general authorization for executive action, leaving the state with a wide margin to maneuver while still being able to brandish the mantle of legality. For Cohn, this raises rule of law and democracy concerns. In this review essay, I suggest that we should follow Cohn’s twin themes through to their logical conclusion. Perhaps controversially, I suggest that where this takes us is not exactly where Cohn ends up. Rather than countering strong executives with strong courts, we should adopt the plural approach Cohn mentions in passing at the outset of the book. This means exploiting institutions’ strengths and attempting to respond meaningfully to their weaknesses. In doing so, we must be careful not to adopt responses to executive power that erect unnecessary obstacles to governance in the public interest.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".