Obstacles to scientific input in global policy
Bibliographic record
Abstract
The United Nations identifies the drivers of the planetary crisis as climate change, biodiversity loss, and pollution. Mitigation requires reliable science to inform decision-making. However, relevant research is often underutilized in policy planning and implementation because policy-making entities limit the ability of scientists to contribute to the process. The United Nations Environment Programme (UNEP) welcomes the participation of independent scientists whose work is free of conflict of interest, but acquiring eligibility is difficult for many scientists. Scientists affiliated with government-funded institutions can seek other modes of entry to UNEP meetings, such as joining national delegations or nongovernmental organizations (NGOs). However, participating in this manner undermines scientists’ ability to operate independently, given that their true affiliations might be obscured. In addition, because some NGOs might be branded as activists, the credibility of scientists’ policy recommendations may be questioned.A preferable option for scientists affiliated with government-funded institutions is to register through accreditation not directly with UNEP, but under multilateral environmental agreements, such as the Basel, Rotterdam, and Stockholm (BRS) Conventions. This option is available to everyone but is underused. Because the requirements are less stringent, scientists are more likely to gain eligibility. Institutions can also register through this process.
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 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 | 0.050 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.110 | 0.081 |
| Insufficient payload (model declined to judge) | 0.020 | 0.013 |
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".