Bench Strength: How Powerful Are Supreme Courts in Common Law Countries Really?
Bibliographic record
Abstract
This thesis examines the extent of judicial power exercised by the top constitutional courts in common law countries, using judicial review as a proxy for this power. I analyze three apex constitutional courts—the supreme courts of the United States, India, and Canada—as they all engage in strong-form judicial review and have been referred to at different times by various scholars as “the most powerful court in the world.” To frame my analysis, I explore how these courts have evolved in power throughout their histories. I employ comparative historical analysis (CHA) and construct a typology that accounts for how these courts, initially weak institutions lacking the power of enforcement and financial resources, develop influence and legitimacy over time through comparative historical analysis (CHA) and a typology that reflects their evolution. I find that while these courts exert more influence and are more consequential as constitutional stakeholders than they were at their inception, they still face inherent weaknesses and engage in arbitrage with the other branches of government to accrue legitimacy and power. Thus, while strong-form courts appear stronger than at their inception, claims of juristocracy are not validated.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".