Hearing the 'Little Guy' - Litigant Involvement to Promote Alternative Dispute Resolution Mechanisms in India
Bibliographic record
Abstract
To tackle the crippling judicial backlog of the Indian justice system, alternative dispute resolution mechanisms have been formalised and introduced in various formats. Despite their obvious benefits of purported lower costs, and timeliness, these mechanisms have not really found their envisioned success and high utility in reducing mainstream litigation. This paper explores how the absence of proper stakeholder engagement, especially with the service users (namely existing and potential litigants) has been an impediment to improving the popularity of ADR mechanisms in India. It studies a similar project conducted in Alberta, Canada, focusing on how litigants provided valuable insights into improving access to justice through free legal aid and services. It proposes a similar community-based model to re-envision and redeploy ADR frameworks within the country, making them appropriate dispute resolution mechanisms, instead of alternatives. While the notion of litigant awareness and involvement have been part of Indian legal scholarship for some time now, this paper attempts to broach these subjects from a sociological empirical researcher 's perspective to better inform judicial reforms in India.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.010 |
| 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".