Bibliographic record
Abstract
Relative to developed countries, there are far fewer women than men in India.Estimates suggest that among the stock of women who could potentially be alive today, over 25 million are "missing".Sex selection at birth and the mistreatment of young girls are widely regarded as key explanations.We provide a decomposition of missing women by age across the states of India.While we do not dispute the existence of severe gender bias at young ages, our computations yield some striking findings.First, the vast majority of missing women in India are of adult age.Second, there is significant variation in the distribution of missing women by age across different states.Missing girls at birth are most pervasive in some northwestern states, but excess female mortality at older ages is relatively low.In contrast, some northeastern states have the highest excess female mortality in adulthood but the lowest number of missing women at birth.The state-wise variation in the distribution of missing women across the age groups makes it very difficult to draw simple conclusions to explain the missing women phenomenon in India.20 6.1 131 40.8 47 14.8 122 38.2 Assam 0 0 21 26.1 23 28.6 36 45.3 Uttar Pradesh 81 14.2 181 31.7 125 22.0 183 32.0 Himachal Pradesh 3 14.2 3 16.6 3 14.0 10 55.2 Orissa 23 19.9 19 16.5 21 18.4 52 45.3 West Bengal 2 1.1 37 17.3 46 21.5 129 60.0 Karnataka 20 16.6 20 16.7 22 19.0 57 47.7 Rajasthan 48 43.4 30 26.8 12 10.8 21 19.0 Gujarat 25 25.8 23 24.2 14 14.7 34 35.3 Andhra Pradesh 25 17.1 20 14.0 23 15.8 77 53.1 Tamil Nadu 8 9.5 5 6.1 15 17.1 59 67.2 Kerala 8 20.3 2 4.4 2 6.1 29 69.3 India 265 11.9 558 25.0 398 17.8 1013 45.3 TABLE 4. Missing Women by Indian State and Age Group, 2003 (in 000s) Sources.National Family Health Survey (2005)(2006), United Nations, World Health Organization, Sample Registration System (Government of India).
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".