The Ironic History of a Grand Policy Experiment
Bibliographic record
Abstract
In n the late 1980s, there was growing concern in the United States and other countries that acid precipitation—the result of emissions of sulfur dioxide (SO2) and, to a lesser extent, nitrogen oxides (NO x) reacting in the atmosphere to form sulfuric and nitric acids —was damaging forests and aquatic ecosystems, particularly in the US Northeast and southern Canada. In the United States, flue gas emissions from coal-fired, electric generating plants were the primary source of SO2 emissions and a major source of NO x emissions. In response to this and other concerns, the US Congress passed and President George H. W. Bush signed into law the Clean Air Act Amendments of 1990. Title IV of this law (which took up only 16 percent of its total pages) launched a grand experiment in market-based environmental policy: the path-breaking SO2 allowance trading program. The concept of allocating permits to emit a certain quantity of pollution that would phase down over time, while allowing permit-holders to trade their permits, is now broadly familiar. But two decades ago, this cap-and-trade approach to environmental protection was quite novel. Many in the environmental commu-nity—with the prominent exception of the Environmental Defense Fund—were hostile to the notion of trading “rights to pollute”; others doubted the workability of such a scheme. Nearly all pollution regulations took a much more prescriptive
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 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 | 0.025 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".