Social Security Measures and Informal Sector in India: An Overview
Bibliographic record
Abstract
The Indian labour market is primarily informal, both historically and currently. The informal sector is often referred to as the 'unorganized sector', and individuals employed in this sector are commonly referred to as 'unorganized workers'. Over 90% of people in India's labour market were engaged in informal employment. This paper attempts to examine the existing labour laws, acts, and social security measures designed for marginal communities in the unorganized sector. Study findings suggested that measures have been successful efforts in terms of providing the integrated database for unorganized workers under the eShram initiative of the government of India. Although schemes provide social security, there has been inconsistency in the performance of the schemes. The study recommends that proper awareness of the scheme should be planned to reach the potential beneficiary, as well as a robust monitoring mechanism for the evaluation of the measures by adopting both top-down and bottom-up approaches.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".