The human rights based approach to climate change mitigation: legal framework for addressing human rights questions in mitigation projects
Bibliographic record
Abstract
Over the last decade, the effects of an unprecedented rise in global temperature due to climate change, on the enjoyment of human rights, especially the right to life, have been subjects of intensive scholarly attention. Gallons of juristic ink have been spilled on the need for States to adopt policy measures aimed at combating climate change. However, recent findings show that policy measures and projects aimed at mitigating climate change are in turn producing even more serious human rights concerns, especially in developing countries. These human rights issues include: mass displacement of citizens from their homes to allow for climate change mitigation projects; lack of participation by citizens in project planning and implementation; citing and concentration of projects in poor and vulnerable communities; lack of governmental accountability on projects and the absence of review and complaint mechanisms for victims to obtain redress for these problems. These secondary human rights impacts of policy measures and projects aimed at mitigating climate change have not received sufficient attention in existing literature. The aim of this research is to examine and analyse the effects of climate change mitigation projects, specifically Clean Development Mechanism (CDM) projects, on the enjoyment of fundamental human rights. It considers how lessons from the approval and execution of CDM projects could inform thoughts on the value and requirements for mainstreaming human rights safeguards into international climate change regimes in general. It analyses the legal and theoretical prospects and paradoxes of adopting the United Nations Human Rights Based Approach (HRBA) as a framework through which human rights standards may be systemically integrated and mainstreamed into extant and emerging international legal regimes on climate change.
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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 | 0.053 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.010 | 0.089 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.026 | 0.015 |
| 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".