Conference diplomacy in the era of COVID: How do international environmental fora adapt to virtual formats?
Bibliographic record
Abstract
Les négociations environnementales internationales ont été bouleversées par la pandémie de la COVID 19. La fermeture des frontières et les mesures de distanciation étant la norme, la gouvernance internationale a dû s'adapter. De nombreux forums ont choisi de mener les négociations de manière virtuelle. Dans cet article, nous analysons les défis et les opportunités des négociations multilatérales virtuelles vis-à-vis de la gestion du processus de négociation. Nous mettons l'accent sur les négociations virtuelles au sein de la Convention sur la diversité biologique, du Protocole de Montréal et de la Convention-cadre des Nations Unies sur les changements climatiques. Les données sont collectées à partir de documents officiels, de rapports de presse spécialisés et d'entretiens semi-structurés. Les résultats montrent que l'impact des formats virtuels est particulièrement élevé sur la transparence et l'inclusivité des petits groupes et la transparence vis-à-vis l’agenda des négociations.
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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".