Operationalising Integrated Coastal Zone Management and Adapting to Sea Level Rise through Coastal Law: Where Does India Stand?
Bibliographic record
Abstract
Abstract With an overwhelming majority of India’s population living on the coast and depending on coastal resources for their sustenance and livelihood, sustainable coastal development is of critical importance to this country. India is also susceptible to the impacts of climate change and sea level rise. This article reviews India’s attempts to manage its long coastline and coastal resources through the instrumentality of law. The salient features of its first coastal law of 1991 and the subsequent draft law of 2008, culminating with a new law adopted in 2011, are analyzed in detail. Specifically, this article examines how these laws provide for integrated coastal zone management, the primary methodology to attain sustainable coastal development, and how they further adaptation to sea level rise. The article argues that, as it stands, India’s coastal law is ineffective to further these two objectives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".