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Early Pioneers in Natural Resource Economics

2016· article· en· W2345298043 on OpenAlexaff
Gardner Brown, V. Kerry Smith, Gordon R. Munro, Richard C. Bishop

Bibliographic record

VenueAnnual Review of Resource Economics · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEconomicsNonmarket forcesNatural resourceSalience (neuroscience)BequestValuation (finance)Natural capitalManagerial economicsMicroeconomicsContext (archaeology)Public economicsPositive economicsNeoclassical economicsApplied economicsEcosystem servicesComputer sciencePolitical scienceEcologyLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.837
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.028
GPT teacher head0.209
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2016
Admission routes1
Has abstractyes

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