Comparative Analysis and Legality of Anti-poaching Agreements in the Indian Context
Bibliographic record
Abstract
Anti-poaching and wage-fixing agreements in labour markets represent a significant, yet largely unaddressed, antitrust concern in India. This paper argues that the Competition Commission of India (CCI) possesses both the legislative mandate under the Competition Act, 2002, and a compelling jurisprudential basis to proactively investigate and penalise such collusive practices. Drawing on a comparative analysis of enforcement trends in jurisdictions like the US, EU, and Canada, and a doctrinal review of Indian contract and competition law, this paper contends that the CCI’s current reluctance to engage with these issues, often deferring them to employment law, is a critical lacuna. It demonstrates that existing provisions, particularly section 3 of the Competition Act, are sufficient to address these anti-competitive agreements. The paper concludes by offering specific policy and enforcement recommendations for the CCI, urging a shift towards robust scrutiny to safeguard labour mobility, to ensure fair wages, and promote overall economic efficiency in India’s rapidly evolving labour markets.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".