RIGHTS OF NATURE FRAMEWORK AN APPROACH TOWARDS PROMOTING ECOSYSTEM PROTECTION AND RESTORATION IN THE PHILIPPINES
Bibliographic record
Abstract
This paper aims to present and introduce the Rights of Nature concept as a growing global framework movement that seeks to protect the environment by recognizing that nature, particularly ecosystems, like humans, have intrinsic rights to live. The Rights of Nature framework acknowledges that ecosystems and natural communities are not mere assets subject to ownership; instead, they are living entities with an inherent and unassailable right to thrive and endure. Its mission is to promote the preservation and revitalization of ecosystems. As of 2022, 24 countries, including Colombia, New Zealand, Bangladesh, Australia, India, seven Tribal Nations in the U.S. and Canada, and over 60 U.S. cities and counties, have introduced laws recognizing nature's rights. In various parts of the world, local and national courts have granted ecosystems the status of living beings and, in some cases, personhood. New laws are continuously being developed to regulate and protect the natural environment. These laws have changed how we view ecosystems and natural communities, giving them rights that can be enforced by people, governments, and communities working to benefit nature. In 2018, the Rights of Nature have been adopted in the Philippines. It is now a growing movement in the Philippines, and they are also pushing for a bill called the Rights of Nature Act 2022 that aims to protect the country's ecosystems by giving legal personhood to nature.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".