Bibliographic record
Abstract
The purpose of this concluding chapter is not merely to reprise the major arguments made in the book and condense its main thesis. Instead, building on the arguments made in the book, this chapter aims to be future-guiding and discusses a potential alternative approach to labour law, particularly from an Indian and more generally from a Global South perspective. It is hoped that some of the future-guiding reflections offered in this chapter will aid the way labour law is debated, practised and interpreted in the country, by both policy and legal practitioners, including the judiciary. Of course, some of the main arguments of the book are revisited here, but they occupy a limited part of this chapter. In revisiting the main arguments of the book, the intention is to emphasise the understanding of labour law in light of the specific requirements under the Constitution of India. In discussing an alternative approach to labour law, this chapter also indicates some potential areas for future scholarly explorations. After this more general introduction to the chapter, in the first section, the foundations of labour law in India, as conceived under the Constitution, are revisited. Drawing on this discussion, the second section offers a more general take on the alternative social justice-based theorising of labour law. The third section is devoted to the discussion of the overall orientation of the 2020 labour law reforms. The chapter ends (fourth section) with a note on overcoming the dependent juridical mentality, which has historically constrained an alternative conceptual formulation of labour law in India, in view of the contextual realities of the country. This book argued that labour law furthers the social justice mission of the Constitution, which is conceived in relation to social cooperation among worker-citizens of the country. Labour law is, then, an instrument to practically realise the constitutional commitment made to worker-citizens. In so realising the constitutional commitment, the challenge for labour law is to sustain the balance among the different aspects of the social justice mission envisaged under the Constitution. It is this balance among the three components of social justice – market freedom, social solidarity and participatory deliberation – that gives the idea of social justice its unique Indian character.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".