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
Similar to other policy issues, climate change policy proceeds in a cyclical fashion that proceeds from agenda setting, to policy development, to implementation, and finally to monitoring and review. Agenda setting involves politicians becoming convinced, usually by the science but also by politics and public opinion, that the climate issue deserves a policy response. Policy development involves a great deal of economic and policy option assessments that are winnowed down to a few options that may have “political traction” (i.e. those politicians think might succeed). Policy implementation involves turning policies into law and regulations that industry and individuals will act upon. Policy review, especially monitoring outcomes, is perhaps the most important phase, and for the climate change issue, the ongoing conclusion to date seems to be that more needs to be done, leading to the policy cycle starting over again. But there are also disturbing signs that this “top-down” approach is no longer working, and more “bottom-up” approaches, linked to the energy sector and clean technology, may become important new forces in forging action on climate change.
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 | 0.000 | 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) | 0.003 | 0.004 |
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