A COMPARATIVE STUDY ON EXPERT OPINION UNDER EVIDENCE LAWS : INDIA AND DEVLOPED COUNTRY
Bibliographic record
Abstract
The expert opinion evidence is vital in making a decision in the courts when the dispute cannot be solved without scientific, technical or specialized knowledge that an ordinary judge cannot possess. In India, expert evidence is admissible and used under the Bharatiya Sakshya Adhiniyam, 2023, especially, Sections 39 to 45, which accepts expert opinion as evidence but does not hold it as conclusive. In India, courts have always argued that expert testimony is only advisory and should be looked upon critically and supported with other evidence to make it reliable and fair. This paper reviews the Indian legal system on expert opinion evidence and provides a comparative perspective with the situation in the United States, the United Kingdom, Canada, Australia, and the European Union. The paper sheds light on the major variations in admissibility requirements, judicial gatekeeping functions, expert neutrality, and accreditation processes that it has identified through a review of statutory and leading judicial cases. Whereas other jurisdictions like the United States and Canada have structured admissibility tests based on scientific reliability and relevancy, the Indian system does not have a uniform standard, formal accreditation of experts, and no real protection against partisanism. Keywords: Evidence Laws, Expert opinion, The Bharatiya Sakshya Adhiniyam, 2023, International stander, gatekeeping, scientific innovation.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".