Litigating the Arbitration Clause: Considering Uber-Driver Arbitration in India
Bibliographic record
Abstract
In Canada, hundreds of Uber drivers came together for a class action lawsuit against Uber seeking benefits under Ontario employment laws. In India, drivers engaged by an instant delivery service platform went on strike against a new pay structure under which the minimum pay-out per delivery was reduced, leading to a drop in the earnings of the service provider by 50% per day. The terms of service form part of standard form non-negotiable agreements. Companies that include a mandatory arbitration clause with such standard form contracts are increasingly facing lawsuits across jurisdictions. The Canadian Supreme Court held such an arbitration clause invalid, largely on the ground that it was part of a ‘standard form contract’ that was ‘non-negotiable’ and ‘realistically unattainable’. Enforcing such arbitration clauses with users have had mixed success, leading to business uncertainty and raising transaction costs due to litigation across jurisdictions. This article explores implications of decisions across jurisdictions regarding such arbitration clauses in the Indian context. The article then argues for a universal harmonization of rules from a global perspective by formulating a general principle to afford certainty to businesses operating at a global level.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.014 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.013 | 0.015 |
| 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".