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Record W4319071092 · doi:10.3197/jps.63799953906865

Overshoot

2023· article· en· W4319071092 on OpenAlexaff
William E. Rees

Bibliographic record

VenueThe Journal of Population and Sustainability · 2023
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOvershoot (microwave communication)ThrivingPopulationHomo sapiensNatural resource economicsEconomicsGlobal warmingDevelopment economicsBusinessClimate changePolitical scienceEnvironmental ethicsNeoclassical economicsEcologyGeographyComputer scienceSociologySocial science

Abstract

fetched live from OpenAlex

The human enterprise is in overshoot; we exceed the long-term carrying capacity of Earth and are degrading the biophysical basis of our own existence. Despite decades of cumulative evidence, the world community has failed dismally in efforts to address this problem. I argue that cultural evolution and global change have outpaced bio-evolution; despite millennia of evolutionary history, the human brain and associated cognitive processes are functionally obsolete to deal with the human eco-crisis. H. sapiens tends to respond to problems in simplistic, reductionist, mechanical ways. Simplistic diagnoses lead to simplistic remedies. Politically acceptable technical ‘solutions’ to global warming assume fossil fuels are the problem, require major capital investment and are promoted on the basis of profit potential, thousands of well-paying jobs and bland assurances that climate change can readily be rectified. If successful, this would merely extend overshoot. Complexity demands a systemic approach; to address overshoot requires unprecedented international cooperation in the design of coordinated policies to ensure a socially-just economic contraction, mostly in high-income countries, and significant population reductions everywhere. The ultimate goal should be a human population in the vicinity of two billion thriving more equitably in ‘steady-state’ within the biophysical means of nature.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.311
Teacher spread0.294 · 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.

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

Citations10
Published2023
Admission routes1
Has abstractyes

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