Bibliographic record
Abstract
The transgender community has been historically marginalized in Indian society. They have always faced inequality, disdain, social and economic exclusion. The tradition of giving blessing and getting donations is prevalent in marriage ceremonies, housewarming, birth, and other ceremonies of newlyweds and this forms the basis of their income. Economically, most of the members of the transgender community are poor. Their income is limited due to lack of education, lack of employment opportunities, and social exclusion. Some transgender members depend on begging, prostitution, and other informal work in trains and signals. Although, in the past years, some transgenders have entered government and non-government jobs and other professions, but the number is still very limited. In the field of education, transgender community is facing major challenges. Their access to education is minimal, and they often face inequality, untouchability, and physical abuse in schools and colleges. In NALSA vs Union of India 2014, the Supreme Court recognized transgenders as a third gender, giving a new identity to the transgender community. The way has been cleared for special rights and welfare schemes for the transgender community. Schemes and programs are being run for the welfare of the transgender community in various states, such as Garima Grah, Abhudaya Cell, Special Police Assistance Center, but their implementation has been slow and limited. India ranks eighth among the popular countries of the transgender community, while Spain ranks first, Sweden second, Argentina third and Canada fourth. The population of the transgender community in entire India is estimated to be 4.88 lakhs. About 28% of the transgender population lives in Uttar Pradesh alone. In other states, more than 66% of transgenders live in rural areas. Their literacy rate is less than 56 percent (Mal & Mundu, 2023).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".