India’s Public Policies for Providing Human Security to its Citizens: An Analysis
Bibliographic record
Abstract
The term 'human security’ has to be about protecting people from foreign military aggression, genocide, ethnic cleansing, sectarian warfare, terrorism, violent crime, or other human rights violations as well as from extreme poverty or disease. This kind of human security is applicable to India as well. The first major statement concerning human security appeared in the 1994 Human Development Report, an annual publication of the United Nations Development Programme (UNDP). "The concept of security," the report argues, "has for too long been interpreted narrowly as security of territory from external aggression, or as protection of national interests in foreign policy or as global security from the threat of nuclear holocaust....Forgotten were the legitimate concerns of ordinary people who sought security in their daily lives". So, the human security significance raised. But both national and human security are used as interchangeably, when implementing public policies in a state, which includes India. There are different perspectives on human security available such as UN, Canada and Japan etc. But the first two are regarded as the major perspectives in this area. The public policies implementation for providing human security to its citizens had been very successful in 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".