Early Pioneers in Natural Resource Economics
Bibliographic record
Abstract
This review focuses on four key scholars who were instrumental in helping to launch the field of natural resource economics: Siegfried von Ciriacy-Wantrup, James Crutchfield, John Krutilla, and Anthony Scott. Their contributions include recognizing natural resources as renewable capital, thereby altering the important dynamic dimensions of an efficient allocation. The introduction of irreversibility was a key element for decisions involving unique natural assets. Introducing uncertainty into these choices required consideration of appropriate public attitudes toward risk that led to the concept of a safe minimum standard. Identifying and emphasizing the salience of nonmarket values, particularly existence and bequest benefits, gave rise to the contingent valuation and the development of stated preference methods as a cottage industry. Setting forth and evaluating alternative management policies for open access resources in a dynamic context were other achievements. The backgrounds of these four scholars shaped their professional orientation; their contributions, in turn, have shaped the evolutionary path of resource economics research.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".