Youth Empowerment through Skill Development: A Study on Skill Development Initiatives
Bibliographic record
Abstract
India has a distinct advantage over advanced economies in having a larger percentage of a young workforce. However, the developed and the developing economies show a similar trend in the declining percentage of youth joining the workforce. For a large chunk of the youth, the lack of basic skills needed for employment after completing school or graduation does not present a rosy scenario. Some of the reasons could be a very dismal connection between formal and vocational education, meagre training facilities, under-qualified trainers and lack of infrastructural facilities. For countries like India sustained efforts have to be made by the successive Governmental and other agencies in the field of basic education and Skill development to provide basic education and enhance the skills of the un-employed youth. 'Skill' is the ability to do something well, and the current situation calls for a quick reorganization of the skill development ecosystem. It will act as a defining element in India's growth story by becoming an essential ingredient for future economic growth if India is to transform into a diversified and internationally-competitive economy. To improve Skill development ecosystem, Government of India has launched a programme ‘Skill India’ in 2015. ‘Skill India’ on par with ‘Make in India’ is a dream project of Prime Minister Narendra Modi. The main focus of this study is to analyze the skills required by the individuals for making themselves employable and training them on those skills to meet the requirements of the companies in various sectors. The paper also focuses on the various Skill Development initiatives taken by the Government of the India.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".