A Rose by Any Other Name: Understanding Judicial Decisions that Do Not Cite Precedent
Bibliographic record
Abstract
In common‐law countries, legal precedent serves as a foundation of judicial opinions. Judges cite precedent to explain their decision, and it is this use of precedent that threads one decision to another. The Supreme Court in India stands in contrast to its counterparts in other countries in that it annually decides not dozens, but thousands, of cases. Perhaps unsurprisingly, nearly half the Court's decisions do not cite any precedent at all. This article examines this phenomenon, specifically how it affects judges’ commitment to the common law, in substance if not in form. Examining every Court decision for the period 1950–2010, we textually analyze the opinions using machine learning to determine what connection, if any, exists between cases. We find that it is possible to accurately model how the Court cites to existing precedent and that even for decisions without any citations, there is almost always at least one prior decision the Court could have cited. Our finding suggest that time and resource demands are primarily responsible for the failure to cite relevant precedent, but that the Court acts efficiently, given the constraints placed on it, in deciding in which decisions to include precedent. This research, however, leaves unanswered whether the Court provides sufficient guidance to lower courts.
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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 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".