Bibliographic record
Abstract
W hen President Bush announced the nomination of Utah governor Michael Leavitt for administrator of the US Environmental Protection Agency (EPA), the immediate response was a confused murmur.Leavitt, the nation's longest-serving governor, has performed admirably for Utah in the last decade, and is popular among his fellow governors.But who is he, and what would he bring to the EPA?The answer may lie in a word you've never heard of.Leavitt is no neophyte to the land-use arena, but is most notable for his views on the environment, which he encapsulates in the term "Enlibra", from the Latin (loosely) for "in balance".The brainchild of Leavitt and former Oregon Governor John Kitzhaber, Enlibra could be called a new vision for protecting air, land, and water.The Salt Lake Tribune also called it a "woozy brew of market-based economics, local empowerment, cost-benefit analyses, collaborative decision making, and incentive-driven processes".Leavitt envisions Enlibra as a way to reach the middle ground between conservationists and industry.In theory, the strategy tries to bring some sanity to environmental issues by balancing conservation and development interests, regulators and the regulated.It holds that big, topdown regulation is inefficient, slow, and expensive, instead favoring state and local control of regulation.This results in more flexibility and participation and less delay -and, say critics, much less conservation.Are these audacious and ambitious goals or so much hot air?Enlibra's guiding principles vary from the bland and obvious to the practical and exciting.All eight involve varying degrees of conflict to implement fully at the national level."National Standards, Neighborhood Solutions -assign responsibilities at the right level."A lot of experts have said that states should take the lead in environmental regulation and enforcement.Enlibra may be stating the obvious, but implementing national standards locally has a nice ring to it.Of course, Congress has written the laws so that state and local responsibility are kept on fairly short leashes."Collaboration, Not Polarization -use collaborative processes to break down barriers and find solutions."The collaborative process is nothing new; the EPA has dozens of outreach programs.Collaboration between some NGOs and companies, though, may be well-nigh impossible.Both camps have valid opinions, and discussion would be better than the current climate of vitriol and lawsuits."Reward Results, Not Programs -move to a performancebased system."This means environmental management should focus on outcomes, not on programs, initiatives, and processes as they tend to do now.Federal, state, and local policies should encourage "outside-the-box thinking" and "solving problems . . .should be rewarded".If this sounds like empty business-speak, you're right.Not a bad thought, though, if it is for real.Only time will tell, but this doesn't seem particularly efficient or timely.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.559 | 0.302 |
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".