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Record W2169826340 · doi:10.1093/scipol/scs029

Governing the Air: The Dynamics of Science, Policy, and Citizen Interaction by Rolf Lidskog and Goran Sundqvist

2012· article· en· W2169826340 on OpenAlex
Camille Callison

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience and Public Policy · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGöranPolitical scienceCorporate governanceConventionPublic administrationInternational relationsEnvironmental ethicsSociologyRegional scienceManagementLawEconomicsHumanitiesPolitics

Abstract

fetched live from OpenAlex

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.

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.

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 armCategoriesStudy designConfidence
gemmaScience and technology studies
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptScience and technology studies
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
models agreeAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.831
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.278
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it