Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Global negotiations to reduce greenhouse gas (GHG) emissions have so far failed to produce an agreement. Even if negotiations succeeded, however, a binding treaty could not be ratified or implemented in many nations due to inadequate public support for emissions reductions. The scientific consensus on the reality and risks of anthropogenic climate change has never been stronger, yet public support for action in many nations remains weak. Policymakers, educators, the media, civic and business leaders, and citizens need tools to understand the dynamics and geopolitical implications of climate change. The WORLD CLIMATE simulation provides an interactive role-play experience through which participants explore these issues using a scientifically sound climate policy simulation model. Participants playing the roles of negotiators from major nations and stakeholders negotiate proposals to reduce GHG emissions. Participants then receive immediate feedback on the implications of their proposals for atmospheric GHG concentrations, global mean surface temperature, sea level rise, and other impacts through the C-ROADS (Climate Rapid Overview and Decision Support) policy simulation model used by negotiators and policymakers. The role-play enables participants to explore the dynamics of the climate and impacts of proposed policies using a model consistent with the best available peer-reviewed science. WORLD CLIMATE has been used successfully with students, teachers, business executives, and political leaders around the world. Here, we describe protocols for the role-play and the resources available to run it, including C-ROADS and all needed materials, all freely available at climateinteractive.org . We also present evaluations of the impact of WORLD CLIMATE with diverse groups.
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.
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 | 0.001 | 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 | 0.001 | 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) | 0.001 | 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 it