Digital bazaars reimagined: Comparing platform work regulations in South Asia through the lens of the EU PWD and ILO standard-setting procedure
Bibliographic record
Abstract
This article examines the regulatory approaches to platform work in India, Pakistan, and Bangladesh. Our analysis explores a critical socio-legal gap in labour law scholarship by providing a comprehensive comparative overview of the platform labour regulations across these three major South Asian economies. It reveals divergence in practices and approaches within the South Asian region, characterised predominantly by laissez-faire , soft law, and deregulatory frameworks. We also demonstrate how the platform economy has accentuated and exacerbated pre-existing informality throughout South Asia, which is grounded in the understanding that insufficient regulatory oversight perpetuates this informality in the labour markets even more. Meanwhile, the recently adopted EU Directive 2024/2831 on improving working conditions in platform work (PWD) has emerged as a global benchmark for platform regulation. Our analysis examines three key areas of the PWD, namely, the presumption of employment, algorithmic management oversight, and collective rights safeguards across South Asian jurisdictions. We find that the existing regulatory framework in the region largely does not incorporate these regulatory aspects and provisions due to structural and institutional constraints, compounded by a deregulatory orientation. Therefore, we examine recent ILO platform standard-setting to evaluate its potential impact on South Asian jurisdictions, with the aim of transposing these principles through an international labour standard. We contend that the ILO standards might provide a promising framework for regulating platform work in these contexts only if it clearly reiterates the right of platform workers.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".