Bibliographic record
Abstract
A large number of regional, national, and international institutions are concerned with the governance of the environment or are tasked with managing and finding solutions to the effects of environmental problems arising from, for example, pollution or deforestation. Some of these have responsibility for broader aspects of ecosystem management, biodiversity conservation, the protection and conservation of wildlife, the sustainable use of resources, the monitoring of long‐range air pollutants, or the mitigation of and adaptation to climate change. Others have a specific mandate in relation to forests, oceans, fisheries, or particular species of animal such as polar bears or whales. International procedures for environmental governance, especially those that cut across national boundaries and jurisdictions, or that deal with migratory species, have arisen from agreements signed between countries, or have emerged in the form of conventions that set out principles for how an ecosystem or a species is to be protected, conserved, and managed. Countries that ratify and become signatories to international conventions, such as those agreed upon by the United Nations, are legally bound to observe their principles and to act within the rules and regulations that have been set out, often following considerable political negotiation.
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 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.062 | 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; both teacher heads agree on what is shown here.
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".