Governing the Air: The Dynamics of Science, Policy, and Citizen Interaction by Rolf Lidskog and Goran Sundqvist
Bibliographic record
Abstract
With the emergence of climate change as an issue of global concern, governance challenges related to transboundary air pollution provide a rich opportunity for analyzing approaches to international policy. This edited volume takes up the gauntlet with varied analyses of the Convention on Long-Range Transboundary Air Pollution (CLRTAP) and successive air policies in the EU. The volume grew out of a 2005 workshop that sought to foster dialogue between ‘scientists, experts, decision makers, and citizens’, as part of the Swedish Foundation, Mistra's Programme on International and National Abatement Strategies for Transboundary Air Pollution. Rolf Lidskog and Göran Sundqvist, the editors of this volume, are sociologists from Sweden with over two decades of research on the role of expertise and shaping of environmental governance each. They have undertaken an ambitious program of ‘cross-fertilizing’ the fields of international relations (IR) and science and technology studies (STS). This volume is part of a larger MIT Press series that has similar goals, and is edited by Sheila Jasanoff and Peter Haas. Haas also contributes a co-authored chapter to this volume.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Science and technology studies Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".