The Science and Politics of Global Climate Change: A Guide to the Debate
Bibliographic record
Abstract
The Science and Politics of Global Climate Change: A Guide to the Debate, Andrew E. Dessler and Edward A. Parson, Cambridge: Cambridge University Press, 2006, pp. 190. Among policy issues struggling for attention on political agendas, climate change is particularly consequential, by virtue of its large-scale negative consequences for all human communities and ecosystems and the high policy costs of remedial action. The stakes are singularly high, yet the general public is not well informed about the reality of climate change. Even the concerned citizen seeking information gets lost between tendentious sketches in the mass media, on the one hand, and practically illegible specialized literature, on the other. Dessler and Parson's work is a welcome middle ground that provides clearly comprehensible scientifically validated information on all aspects of the issue. The book summarizes and evaluates current information on climate change, focusing primarily on multilateral scientific assessments conducted by the Intergovernmental Panel on Climate Change. It offers a balanced review of the state of knowledge, and carefully delineates the bounds of scientific agreement and uncertainty.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".