Bibliographic record
Abstract
Globally, there was a dramatic and disproportionate increase in caseloads in the second half of the twentieth century due to the expansion of human rights jurisprudence and legal awareness among citizens. This in turn, affected the quality of justice in the Apex Court of every country involved in the process of constitutional review. It was found that there cannot be any generalization in designing a Constitutional Court and it all depended on the constitutional and legal history of that particular nation. In many countries, the legislature and executive brought timely reforms to keep the Apex Court free from backlogs, but some countries, even today, are reeling under the pressure of unresolved cases. India is one among them and of late, the discussion about the National Court of Appeal (NCA) as a solution to this problem has gained momentum. This paper analyses the feasibility of establishing the NCA, along with measures that can be adopted by India, in tackling the mounting arrears of cases in Courts, following the American model of review such as U.S., Canada, Japan, and Brazil.
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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 | 0.066 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.033 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".