Economic Science, Endangered Species, and Biodiversity Loss
Bibliographic record
Abstract
Abstract: Although economic analysis can be used to argue for preservation of species and habitats, many natural assets represent inferior investments in society's asset portfolio. We demonstrate this for the case of ancient temperate rainforests and minke whales ( Balaenoptera acutorostrata ). For both rainforests and whales, we determined their value for harvest and balanced this against society's valuation of the preserved stock. For the market and nonmarket data available, we then determined how much rainforest and how many minke whales global society should keep in its asset portfolio. Although ecologists increasingly attempt to justify preservation of biological assets on economic grounds, we argue that this might be a dangerous approach to take. Ultimately, it may be necessary to reexamine the ethical foundations for conservation of nature and biodiversity, including the economist's use of utilitarianism. We suggest that the safe minimum standard approach may prove useful in practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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; both teacher heads agree on what is shown here.
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".