Bibliographic record
Abstract
If you take a course in environmental economics, you are likely to be dazzled with fancy graphs using isoquants, budget constraints, and even social welfare functions.From these fancy tools, F. Bator went so far as to determine a "bliss point," suggesting that these tools could take society to its maximum level of well-being.I still have the book from which these graphs were taken and I can still remember distinctly sitting through this lecture and just thinking, can you believe it?I am going to learn how to take society to its bliss point!This type of analysis illustrates the way economists often approach problems, namely using marginal analysis to maximize some value subject to opportunity cost constraints.From this analysis follows one of the main tenets of economics: if the marginal benefits are greater than the marginal cost, do it.We economists think this marginal analysis is a pretty powerful way of thinking about the world.In determining how clean the air should be, we need to know what the additional benefits of clean air are, what the additional costs of clean air are, and as long as the additional benefits exceed the additional cost then clean it up.If you want to know whether to save an endangered species, the answer is the same: if the marginal benefits exceed the marginal costs, save it.Let us apply this to the issue of wolf reintroduction into Yellowstone National Park.A few years ago, during the Clinton administration, Bruce Babbitt, then Secretary of the Interior, released a few Canadian timber wolves in Yellowstone with the idea that wolves were an important missing link in the ecosystem.If you asked an economist about whether we should do this and, if so, how many wolves t This essay is based upon a lecture given at Case Western Reserve School of Law on April 7, 2004, sponsored by the Nord Family Foundation, the Property & Environment Research Center, and the Center for Business Law & Regulation.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.009 | 0.025 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".