Crossing Borders, Crossing Boundaries: The Role of Scientists in the U.S.-Canadian Acid Rain Debate
Bibliographic record
Abstract
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.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.060 | 0.040 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".