Bibliographic record
Abstract
The institution of child marriage throughout the nineteenth and early twentieth century not only stripped Indian girls of their agency, but also frequently denied them their education. In 1884, Rukhmabai, a young Indian girl of just eleven-years-old, was married to Dadaji Bhikaji, a man eight years older. Although Rukhmabai was able to resist the forced marriage and eventually went on to become India’s first female doctor, Rukhmabai’s victory was generally an anomaly of the time and reflected a tenacity to attain greater education. Throughout her writings, Rukhmabai expresses deep sadness from being denied the opportunity for an adequate education, and identifies female education as one of the chief disproportionate impacts of child marriage for girls. This project will trace the evolution of child marriage negotiations from the 1891 Age of Consent Act to the 1929 Child Marriage Restraint Act, specifically addressing the way that related discussions allowed Indian women to establish the importance of their adolescent years in their educational pursuit. By uncovering the voices of both child marriage victims and female reformers, we are able to garner an understanding of the changing Indian social landscape at the time and the way that Indian women negotiated their agency against the backdrop of globalization, the nationalist agenda, and caste, religious, and regional differences. This project will stress female adolescence as an evolving concept throughout twentieth century India, and will draw on the important relationship between education and female agency.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".