Checking the Other and Checking the Self: Role Morality and the Separation of Powers
Bibliographic record
Abstract
The concepts of the rule of law, the separation of powers, and checks and balances are related in complicated ways. Jacob T Levy brings this to light in his thought-provoking McDonald Lecture, “The Separation of Powers and the Challenge to Constitutional Democracy.”1 In this response to Levy’s paper I want to further explore the relationship between these three ideas. I will argue that, when thinking about the rule of law, we must consider the idea of “role morality” and its place in constraining power. We should think of the constraints on power that stem from role morality as “internal” as opposed to “external” checks on power. I also suggest that we would do well to broaden our understanding of what the rule of law requires, and to think of it not just as a matter of ensuring impartiality and formal legal equality in the sense that the law applies to all actors within the system. We might benefit from thinking of the rule of law as a weightier moral concept that demands that decision-makers comply with moral ideals, and not just with the rules as laid out.
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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.072 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".