An Analysis On Sharing Economy And The Law Navigating Bankruptcy Challenges in India's Digital Platform
Bibliographic record
Abstract
The sharing economy has reshaped India's digital and labour landscape through platforms like Uber, Swiggy, and UrbanClap, offering flexibility and new income opportunities while challenging traditional legal structures. This paper examines the intersection of the sharing economy and bankruptcy law, focusing on how India's current frameworks particularly the Insolvency and Bankruptcy Code (IBC), 2016 and the Code on Social Security, 2020 address financial distress in platform-based enterprises. It highlights key issues such as ambiguous employment classification of gig workers, lack of algorithmic transparency, and inadequate recognition of digital assets during insolvency proceedings. Through comparative analysis with global frameworks, including the EU's Platform Work Directive and reforms in Australia, Canada, Brazil, and Singapore, the paper identifies crucial legal gaps and offers recommendations for reform. These include clearer worker classification, priority for wage claims, valuation of digital assets, social security contributions, and centralized digital grievance mechanisms. The study argues that while India has made important strides in regulating gig work, the current framework remains insufficient to protect workers in platform insolvencies. Strengthening transparency, accountability, and worker rights is essential to ensure that India's rapidly expanding sharing economy remains equitable, sustainable, and resilient in the face of financial instability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".