Bibliographic record
Abstract
To be completed by December 2007 This paper analyses trends in the latter half of the twentieth century in the heights of Indian women. On average, there was some growth in height at maturity for the cohorts of 1950-1965 but the cohorts born in 1965-1975 experienced no growth. Many of the states exhibit trends similar to the country-average, with the notable exceptions of Kerala, which displayed monotonic growth, and Punjab & Haryana, which started off with the tallest women but then experienced a decline in average height. Both between and within-state variation in height is inversely correlated with infant mortality rates at birth, an indicator of the disease environment. The level of aggregate income at birth, a potential proxy for nutritional status, is positively correlated with height at maturity in the cross-section but this relationship is weak within states. The paper also examines levels and trends by socio-economic group. Higher caste women are taller and this differential is more or less constant over the quarter century for which we have data. Muslim women have grown faster than Hindu women. Height differentials by education and urban/rural location have widened over time although the trend in these differentials is likely to be influenced by compositional change.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.722 | 0.550 |
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".