Bibliographic record
Abstract
Gender discrimination begins at a young age. Girls face a range of structural barriers that contribute to unequal educational and economic performance. In recent decades, India has enjoyed economic and demographic conditions that ordinarily would lead to rising female labour-force participation rates. India’s female labour force participation rate fell nearly seven percentage points to 22.5% between 2004-05 and 2011-12, according to NSSO data. The sixth economic census reiterates these findings. While women make up nearly half the population, they account for only a quarter of workers employed. If the workforce participation rate for women in India was the same as for men, roughly 217 million women would join the labour force. Yet National Sample Survey (NSS) data for India show that labour force participation rates of women aged 25-54 (including primary and subsidiary status) have stagnated at about 26-28 per cent in urban areas, and fallen substantially from 57 per cent to 44 per cent in rural areas, between 1987 and 2011. The present study is based on the secondary data collected from various sources like Ministry of Indian Labour Organisation, World Bank Report, Newspaper, etc. Different age groups or different surveys essentially tell the same story, even though the levels differ slightly. his is an important issue for India’s economic development as India is now in the phase of “demographic dividend”, where the share of working-age people is particularly high, which can propel per capita growth rates through labour force participation, savings, and investment effects. But if women largely stay out of the labour force, this effect will be much weaker and India could run up labour shortages in key sectors of the economy.
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 | 0.003 | 0.001 |
| 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 | 0.001 |
| 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".