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Record W7036939248

Crossing Borders, Crossing Boundaries: The Role of Scientists in the U.S.-Canadian Acid Rain Debate

2000· article· en· W7036939248 on OpenAlexaboutno aff

Bibliographic record

VenueScholar Works (Boise State University) · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSesquiterpenes and Asteraceae Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAcid rainFraming (construction)Science policyEnvironmental policyPublic policyPolicy analysis
DOInot available

Abstract

fetched live from OpenAlex

Examines the science-policy linkage that defined the policy debate over acid rain in the United States.Alm provides a descriptive analysis of the science-policy linkage that defined the policy debate over acid rain in the United States. He focuses on the role that science and scientists played in both defining the acid rain problem as one worthy of policy consideration and in framing the acid rain issue in a way that would prompt action to reduce pollution levels.A major concern of Alm's study are the problems scientists have in connecting to the policy side of environmental debates. He provides in-depth exchanges from the floor of Congress between scientists and policy makers as they debated the merits of reducing acid rain pollution. These exchanges provide special insight into the difficulty that scientists have in communicating the findings of their research to policy makers and the public. In addition, he uses in-depth interviews with the acid-rain scientists themselves to delineate the way they perceive how science is and ought to be linked to the policy world. Finally, Alm looks at the different perspectives offered by United States scientists versus Canadian scientists and natural scientists versus social scientists, and he examines the importance and implications of these differences to the future of environmental policy making in the United States.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.980
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0020.000
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 head0.006
GPT teacher head0.224
Teacher spread0.218 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2000
Admission routes1
Has abstractyes

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