Bibliographic record
Abstract
This was a memorable year for international environmental law-making.While the COVID-19 pandemic continued to pose challenges, the largest wave of postponed meetings was over, and nations took significant steps forward in addressing some of the most pressing global environmental problems.Progress was seen in regard to international efforts to deal with the biodiversity crisis and plastic pollution, in particular.From the perspective of Finland, the biggest transboundary policy efforts this year were made with respect to biodiversity and climate change.In the area of biodiversity, the postponed second phase of the fifteenth Conference of the Parties (COP-15) of the Convention on Biological Diversity (CBD) was held in Montreal in December.The main focus of COP-15 was to find an agreement on the common goals, objectives, and measures of countries to halt global biodiversity loss.Together with the European Union (EU), Finland aimed for concrete and quantitative global biodiversity targets.The Kunming-Montreal Global Biodiversity Framework to halt biodiversity loss by 2030 was adopted at the Conference.This includes targets to protect 30 percent of the world's terrestrial and inland water and of coastal and marine areas and to restore at least 30 percent of degraded terrestrial and aquatic ecosystems by 2030.The Finnish minister of the environment and climate change (MoE), Maria Ohisalo, compared these commitments to the role of the Paris Agreement in combatting the climate crisis.To ensure proper implementation of the agreed-on goals, Finland and the EU aimed for a strong monitoring mechanism to track the progress of actions taken under the agreement.To this end, a set of indicators common to all nations, to be supplemented nationally, was adopted at the COP.Parties also decided to establish a new fund under the Global Environment Facility to finance biodiversity measures.Finland stressed the need for funds from private sources to complement public funding for biodiversity protection.COP-27 of the United Nations Framework Convention on Climate Change (UNFCCC) was held in Sharm el-Sheikh, Egypt, in November.The main tangible outcome of the COP was the establishment of a new fund to assist parties in the face of damage caused by climate change.However, few other means to accelerate action to tackle climate change were produced.Finland, along with many other nations, was disappointed at the results: 'The decisions made do not reflect the urgency of the need to reduce emissions,' said Minister Ohisalo after the Conference.Finland and the EU advocated for stricter and faster emissions limitations from parties, but these did not receive sufficient support.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".