Impact of Mgnrega on Women Empowerment and Their Issues and Challenges: A Review of Literature from 2005 To 2015
Bibliographic record
Abstract
“National Rural Employment Guarantee Act (NREGA) enacted by legislation on August 25, 2005 and it was renamed as the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) on 2nd October 2009. The MGNREGA has completed ten years since its inception in India”. The aim of the scheme is to enhance livelihood security of the household in rural areas of the country by providing at least 100 days of guaranteed employment in every financial year of every household whose adult member volunteer to do the unskilled work. The purpose of the study is to examine the women empowerment, issues and challenges and impact on MGNREGA scheme in India from 2005 to 2015 and this review paper helps new and young researcher who wants to do research under this area may really helpful to them in order to identify the research problem and research gap. Women participation is very high with 80% of the total beneficiaries under the scheme. The concept of women’s empowerment has got wider popularity and acceptance in Tamil Nadu with the launching of decentralized planning in the state. The study concludes that economically empowering women on MGNREGA scheme lays the basis for greater independence and also for self-esteem. It has become a beacon of light in the empowerment of the rural women and contributed substantially for improving their lifestyle and economic conditions.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".