Out of Many: Military Commissions, Religious Tribunals, and the Democratic Virtues of Court Specialization
Bibliographic record
Abstract
The culture wars of the post-9/11 era have implicated legal systems in multiple ways. The article surveys two such recent episodes, in which questions of institutional design of judicial forums invoked heated disputes regarding the purposes and limits of judicial power in a liberal democracy. The first episode is the ongoing controversy over the legitimacy of military commissions as the forum for trying terrorism suspects in the U.S. The second episode is the failed attempt to award recognition and accommodation to religious (specifically Islamic) tribunals as family arbitration mechanisms in the Canadian province of Ontario. Both episodes seemed to present similarly extreme challenges to the ideal of the ‘rule of law,’ which is largely understood to lie at the core of any legitimate dispensation of judicial power. The article seeks to recontextualize the two cases – military commissions and religious tribunals – as instances in a general institutional trend of court specialization: the diffusion of court forums into multiple units of disparate jurisdictional breadth, professional expertise, and institutional culture. While the pervasive phenomenon of court specialization – usually assessed in terms of institutional efficiency or interest-group politics – is subject to some effective critiques from the rule of law, the article offers a series of arguments from liberal democracy that justify proliferating and diversifying the institutional field of judicial power by way of specialization. When viewed from an overall systemic perspective, jurisdictional multiplicity and diversity are shown to have the potential to promote democratic values of deliberation, pluralism, access, transparency, and accuracy in legal discourse. Once the democratic virtues of court specialization are acknowledged, the disputes over military and religious adjudication are revealed to warrant a more nuanced treatment than current debates have allowed. Further, institutional variations that would realize the benefits of specialization and contain its costs for the rule of law – primarily by establishing modes of interaction among diverse court units – are shown to be available, and are briefly explored. It is there that the discussion over court specialization ought to be heading.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.034 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".