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Record W4385459522 · doi:10.1111/ajes.12533

How the World Economic Forum damages the credibility of climate science

2023· article· en· W4385459522 on OpenAlexaff
Elizabeth Woodworth

Bibliographic record

VenueAmerican Journal of Economics and Sociology · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPoliticsDamagesCredibilityRenewable energyEconomicsPolitical scienceBusinessPolitical economyEconomic policyLaw

Abstract

fetched live from OpenAlex

Abstract The World Economic Forum (WEF) was established in 1971 with the nominal purpose of bringing together leaders to discuss global problems. However, it is well on its way to becoming the most powerful institution in the world, merely by setting forth an agenda for global management that is attractive to many political and business leaders. Two of the central issues on which the WEF has focused are climate change and the pandemic. The first has a depth of scientific support, but the WEF's stance and official pronouncements on the latter were never based on normal scientific review procedures. Since the WEF was a catalyst in the rush to judgment on COVID‐19 lockdowns and vaccine mandates, this suggests that the WEF is primarily guided by political motives, not by science. In fact, the way the WEF approaches the climate issue—as an excuse to restrict freedom without promoting renewable energy sources—makes evident the true motives of the WEF. As a result of the WEF's political use of the climate issue, the validity of climate science has been tarnished in the public mind. If there were true political will to solve the existential problem for future generations, the following could be undertaken immediately: To convert the oil industry to renewable energy, national governments of the world could agree to require that the fossil fuel companies receiving tax dollars convert to renewable energy at a rate of 7%–8% per year. Compounded, within 10 years, these companies would still be dominating the energy game, but with safe sustainable alternatives.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.265
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.025
GPT teacher head0.254
Teacher spread0.229 · 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 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

Citations5
Published2023
Admission routes1
Has abstractyes

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