India Employment Report 2016: Challenges and the Imperative of Manufacturing-Led Growth
Bibliographic record
Abstract
What is the nature of the employment problem that India faces? What kind of economic growth is required to address it? As India posits itself as one of the fastest growing major economies in the world, India Employment Report 2016 examines how the employment challenge undermines the substantial improvement that the economy has made in the last decade and a half. This report provides an in-depth review of the evolving characteristics of the country's labour force, develops new tools for a sharper analysis of the changes in employment conditions, and gives a clearer view of the state of employment in India. Presenting a comprehensive overview of the policy interventions that would be required for the development of India's growth strategy, the report brings out that pursuing a manufacturing-led growth strategy can help the country overcome this formidable challenge. This report has been prepared by the Institute for Human Development (IHD), New Delhi, under the institute's programme on labour markets and employment studies. This is the second report in the series of analytical reports being published biennially by the institute. The present report has been supported by the South Asia Research Network (SARNET) on Employment and Social Protection for Inclusive Growth, which has been initiated by the IHD in collaboration with the United Nations Economic and Social Commission for Asia and the Pacific (UN-ESCAP) and International Labour Organization (ILO) with support from International Development Research Centre (IDRC), Canada.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".