Will there be human rights-based litigation tackling biodiversity loss as a systemic threat?
Bibliographic record
Abstract
With biodiversity steadily declining at unprecedented rates, ecosystem functioning and humanity’s life-supporting safety net are at risk. The irreversible loss of life-supporting ecosystem functions increasingly limits opportunities for the exercise of fundamental rights and freedoms of the younger as well as future generations. As regards climate change and its negative human rights impacts, there are numerous human rights-based climate change cases. We argue that human rights-based litigation addressing the systemic threat of biodiversity decline can learn from such cases. Against the background of the first ground-breaking climate change decision of the German Federal Constitutional Court (FCC) ‘Neubauer et al. vs. Federal Republic of Germany’, we discuss in how far the judicial line of argument can be used in cases regarding countries’ insufficient action to fight biodiversity decline. Particularly the Kunming-Montreal Global Biodiversity Framework (GBF) is analysed in detail with respect to both, the judicial elements needed and the requirements to biodiversity science to provide relevant measures and indicators. We conclude that GBF is very well suited to support human rights-based adjudication against the systemic threat of biodiversity decline.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.020 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".