Female foeticide on rise in India: Causes, Effects and the role of media to overcome this problem.
Bibliographic record
Abstract
Since time immemorial, human civilization has witnessed one thing in common among entire nations on this planet and i.e. exploitation of women. Women who roughly constitute half a human population have been discriminated, harassed and exploited irrespective of the country to which they belong, unmindful of the religion, which they profess, and oblivious of the time-frame in which they live. Though we consider this century as an advanced and modern one, still women are confronting new challenges and facing severe threats to maintain respect, equality and dignity. Female foeticide is perhaps one of the worst forms of violence against women where a woman is denied her most basic and fundamental right i.e. “the right to life”. The phenomenon of female feticide in India is not new, where female embryos or fetuses are selectively eliminated after pre-natal redetermination, thus eliminating girl child even before they are born. Authors Steven D. Levitt and Stephen J. Dubner have argued that if women could choose their birthplace, India might not be a wise choice for any of them to born. It is estimated that more than 10 million female foetuses have been illegally aborted in India. Researchers for the Lancet Journal based in Canada and India stated that 500,000 girls were being lost annually through sex-selective abortions. The land of martyred former Prime Minister Mrs. Indira Gandhi has been killing its daughters by the millions for decades. Nationwide, the numbers of girls for every 1000 boys has dropped from 975 in 1961 to 910 in 2011.The situation is particularly alarming in some of the urban areas of Punjab, Rajasthan, Haryana, Himachal Pradesh and Madhya Pradesh; especially in parts of Punjab, where there are only 300 girls for every 1,000 boys, according to Laura Turquet, Action Aid’s Women’s Rights policy official. It has been widely noticed that Punjabi culture is extremely violent as well as abusive towards the women at large, with no respect for the fairer sex. Hence, this research paper wants to throw light that what are the factors behind Gender-bias and pitiable conditions of the Girl child. How far Indian media is responsible for stereotyping the women and portraying her as an inferior being? What possible roles can government, media and social action-groups can play in women empowerment and to fight the cancerous mentalities which led couples to abort female fetuses. This Research Paper intends to evaluate the problem, causes behind it and to find innovative and peaceful solutions for the biggest challenge 21st century India face.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".