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Record W2634542114 · doi:10.4324/9780203785300.ch18

Ecological Economics and Sustainable Development: Building a sustainable and desirable economy-in-society-in-nature

2015· book-chapter· en· W2634542114 on OpenAlexaff
Robert Costanza, Gar Alperovitz, Herman E. Daly, Joshua Farley, Carol Franco, Tim Jackson, Ida Kubiszewski, Juliet B. Schor, Peter A. Victor

Bibliographic record

VenueView · 2015
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicSustainable Development and Environmental Policy
Canadian institutionsYork University
Fundersnot available
KeywordsExternalityEconomicsSustainable developmentEcological economicsEconomyNatural capitalGoods and servicesMainstreamContext (archaeology)Economic systemGreen economyGross domestic productCapital (architecture)Capital goodSustainabilityEconomic growthEcosystem servicesEcologyMicroeconomicsPolitical scienceGeography

Abstract

fetched live from OpenAlex

The current mainstream model of the global economy is based on a number of assumptions about the way the world works, what the economy is, and what the economy is for (Table 18.1). These assumptions arose in an earlier period, when the world was relatively empty of humans and their artifacts. In this context, built capital was the limiting factor, while natural capital was abundant. It made sense not to worry too much about environmental "externalities," since they could be assumed to be relatively small and ultimately solvable. It also made sense to focus on the growth of the market economy, as measured by gross domestic product (GDP), as the primary means to improve human welfare. And it made sense to think of the economy as only marketed goods and services and to think of the goal as increasing the amount of these goods and services produced and consumed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0040.004
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0090.003

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.

Opus teacher head0.014
GPT teacher head0.217
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations57
Published2015
Admission routes1
Has abstractyes

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