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Record W2984083918 · doi:10.1177/1086026619885111

Invisible Hand or Ecological Footprint? Comparing Social Versus Environmental Impacts of Recent Economic Growth

2019· article· en· W2984083918 on OpenAlexaff
Gregory M. Mikkelson

Bibliographic record

VenueOrganization & Environment · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainable Development and Environmental Policy
Canadian institutionsMcGill University
Fundersnot available
KeywordsDegrowthEcological footprintCorporate governanceGross domestic productEcological economicsConsumption (sociology)Distribution (mathematics)EconomicsSustainabilityDevelopment economicsPublic economicsNatural resource economicsEnvironmental resource managementEconomic growthEcologySociologySocial science

Abstract

fetched live from OpenAlex

This study examines changes in some key indicators among 66 countries on six continents over a 56-year period, to compare the power of economic growth to improve human health and income distribution with its tendency to degrade the natural environment. The results indicate that growth depletes and pollutes nature far more than it benefits society. This suggests that public policy should shift toward enhancement of individual and social well-being in ways more direct and effective, and less ecologically damaging, than reliance on overall growth in gross domestic product. I illustrate this implication with a degrowth scenario for the United States to 2050 that draws on the empirical results for the period 1961 to 2016. And I consider certain reforms in the management and governance of organizations to implement such a scenario.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.002
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.214
Teacher spread0.198 · 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 designObservational
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

Citations27
Published2019
Admission routes1
Has abstractyes

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