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Record W4391179066 · doi:10.3138/jccpe-2-2-002

Herman Daly’s Great Debates

2024· article· en· W4391179066 on OpenAlexaff
Peter A. Victor

Bibliographic record

VenueJournal of city climate policy and economy · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainable Development and Environmental Policy
Canadian institutionsYork University
FundersEconomic and Social Research Council
KeywordsMathematical economicsEconomics

Abstract

fetched live from OpenAlex

Early in his long career, Herman Daly became disillusioned with the priority given to the pursuit of economic growth, especially by rich countries. Instead, he argued for an equitable, efficient, steady-state economy that could be sustained indefinitely on a finite planet. Accordingly, he made proposals relating to human well-being, measuring the economy, population, money and banking, globalization and trade, and for rethinking economics in general, learning from the life sciences and making economics consistent with the fundamental laws of physics. Daly was eager to discuss and debate his ideas with his economist peers, seeking reasons why he might be wrong and making the necessary corrections. This proved much harder to achieve than he anticipated, though he received numerous international awards for his work and was very influential, especially beyond the confines of mainstream economics. However, on several occasions Daly did engage in debates in the academic literature with some very distinguished economists. These included his mentor, Nicholas Georgescu-Roegen, on the longevity of a steady-state economy, recipients of the Nobel Memorial Prize in Economic Sciences Robert Solow and Joseph Stiglitz, on limits to growth, and Kenneth Arrow on overconsumption. With the passage of time, these and his other debates described in this commentary have become increasingly relevant because the excessive environmental pressures that so concerned Daly are greater than ever and rising. And although Daly’s frame of reference was typically national and global, lessons can also be learned from the debates that relate to sub-national regions and cities.

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)0.012
metaresearch head score (Gemma)0.030
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: Review · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0150.028
Scholarly communication0.0130.014
Open science0.0030.007
Research integrity0.0160.022
Insufficient payload (model declined to judge)0.0080.003

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 head0.011
GPT teacher head0.244
Teacher spread0.233 · 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
GenreReview

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

Citations1
Published2024
Admission routes1
Has abstractyes

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