American business interests meet air pollution transport science: understanding the US response to trans-Pacific air pollution
Bibliographic record
Abstract
Since the discovery of air pollution traveling from China to the US during the late 1990s, trans-Pacific air pollution (consisting of a range of non-CO2 greenhouse gases) has been an emerging global environmental issue. But how has it been addressed, how does it relate to the existing multilateral air pollution regime, and who are the interested parties? This article addresses these questions by examining the evolution of the science of trans-Pacific air pollution, discussing the way in which this science has been made policy-relevant by researchers working under the Convention on Long-Range Transboundary Air Pollution, and by illustrating how American economic interests concerned with the effects of trans-Pacific air pollution on American land values and industry have used this scientific knowledge to lobby the US government for regulatory relief. Trans-Pacific air pollution arguably causes regions of the US to violate National Ambient Air Quality Standards, resulting in unwanted federal involvement in local decision-making and tighter regulatory standards, which impedes local economic development and lowers property values. At the same time, laxer environmental standards in China result in increased pollution and lower American industrial competitiveness. The result has been that the US Chamber of Commerce and the Alliance for American Manufacturing have begun to develop policy alternatives.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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, 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".