Protecting the Environment and People from Climate Change through Climate Change Litigation
Bibliographic record
Abstract
Climate change litigation seeks to apply legal rights in order to affect the outcomes that would either mitigate, reduce or can even result in improved alternation to climate change. This article intends to identify and analyse the ways through which the environment and the people are protected from rapid changes in climate through the means of climate change litigation. Protection of the environment as well as people by climate change litigation can be witnessed in various nations throughout the globe particularly Australia, the US, Canada and the UK. The research problem examined in this article shows that the courts are becoming a critical climate change front where climate change conflicts are resolved. The research objectives are to help understand how climate change has impacted human health and the environment and how the courts have stepped into the arena to restrain activities that cause climate change impact. The methodology adopted in this article is both doctrinal and theoretical drawing upon primary and secondary sources of information. The key findings and implications to theory and practice of this article is that it is a medium to foster the jurisprudence of the role of climate change regime through judicial intervention in protecting the environment and people from climate change through climate change litigation.
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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".