Bibliographic record
Abstract
The development of information technology has generated several legal debates. Most of these debates are concerned with substantive rights and obligations. However, the enforcement of these substantive rights and obligations, to a large extent, depends on the effectiveness of procedural laws. This article focuses on one of the most important procedural issues—admissibility of electronic evidence. The factual bedrock for the enforcement of substantive rights and obligations is dependent on the framework we adopt for admissibility of electronic evidence. This article identifies four most important issues surrounding the admissibility of electronic evidence: (i) the debate between special framework and general framework; (ii) the substantive content of the conditions of admissibility; (iii) certification requirements; and (iv) the dichotomy of primary and secondary evidence. For discussing these issues, the experience of the Indian framework lies in the heart of this article. Where relevant, the article also draws lessons from other jurisdictions including the USA, the UK, Canada, South Africa and Australia to discuss the way these debates have been addressed in different jurisdictions.
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 | 0.325 | 0.442 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.012 | 0.089 |
| Scholarly communication | 0.032 | 0.044 |
| Open science | 0.013 | 0.022 |
| Research integrity | 0.026 | 0.033 |
| Insufficient payload (model declined to judge) | 0.004 | 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".