Triaging the Law: Developing the Common Law on the Supreme Court of India
Bibliographic record
Abstract
Legal precedent serves as the foundation of the common law. Judges provide their reasoning through precedent, citing cases to support their conclusion while distinguishing between cases cited by that counsel in favor of an opposing result. Legal precedent also provides the mechanism by which judges communicate with one another, at the same time providing guidance to prospective litigants and the practicing bar. This process is particularly important for supreme courts, whose decisions bind all lower courts within their jurisdiction. For this reason, in most common‐law jurisdictions, the supreme court decides relatively few cases but draws heavily on precedent for the opinions it issues. The Supreme Court in India stands in contrast to its counterparts in countries such as the United States and Canada in that it decides thousands, rather than tens, of cases. Examining the universe of Court decisions from 1950–2010, we find that the Court elects not to cite precedent in nearly half its opinions. In turn, these opinions without citation to precedent are rarely subsequently cited. However, there is a second set of decisions that is more analogous to U.S. Supreme Court decisions. These decisions do cite prior decisions and are cited by later cases. Opinions that do cite precedent gravitate to older opinions, whose salience often endures for decades. These findings suggest the Court is constrained in its ability to process a heavy caseload, and makes strategic decisions as to which opinions to emphasize through its use of precedent.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".