India and the Climate Convention: The Challenge of Sustainable Development
Bibliographic record
Abstract
Introduction\nDuring the last few decades a large number of environmental treaties have\nbeen negotiated at the international level. In the days of globalization, it is\ninevitable that nations all over the world will be involved in the process of\ntreaty negotiation. But while these countries are drawn into the negotiating\nprocess, many are less than prepared to deal with the complex issues\ninvolved. India, too, has been caught up in the global commitment to address\nglobal environmental issues and has been participating in a number of\ninternational treaties. India has signed and ratified, among others, the\nMontreal Protocol on Ozone Depleting Substances,1 and its London and\nCopenhagen amendment, the Basel Convention,2 the United Nations\nConvention on the Law of the Sea,3 the Convention on Biological Diversity,4\nthe CITES Convention,5 and the United Nations Framework Convention\non Climate Change (UNFCCC)6. From a generally defensive role7 in\ninternational environmental treaties, India is moving very slowly towards\na proactive policy. In October 2002, India hosted the Eighth Conference of\nthe Parties to the Climate Change Convention. The act of hosting the\nConference is viewed as "an important capacity building exercise in the\ncountry and will also provide an opportunity to showcase efforts made by\nIndia in the environmental arena/&s;8 Against this background, this paper\nanalyzes the role of India in relation to the Climate Change Convention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".