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Record W3211408268 · doi:10.2021/ju.v1i1.2326

Review of Capitalism, The American Empire, and Neoliberal Globalization by Kenneth E. Bauzon

2021· article· en· W3211408268 on OpenAlexaffabout
Jay Foster

Bibliographic record

VenueThe Journal of Macrodynamic Analysis (Memorial University of Newfoundland) · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional resilience and development
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSummitCapitalismGlobalizationPolitical scienceGenerosityEmpireCapital (architecture)Economic historyEconomic growthDevelopment economicsEconomicsGeographyLawPolitics

Abstract

fetched live from OpenAlex

A t the G7 summit in June 2021, leaders of the top seven “advanced economies” met at a seaside resort in Cornwall, England. After three days of  frolicking on the beach for photo-ops, they emerged promising a billion COVID- 19 vaccine doses for “less well - off”  countries and affirmed $100 billion  per year in “climate finance” from both public and private sources. In short, the  summit —laughably described as a meeting of world “leadership”— was simply yet another lackluster performance piece. The spending on climate change was already promised in 2009, and it pales in comparison to the  trillions  of dollars spent by G7 countries on domestic pandemic relief. Behind the G7 are the legacies of carbon capital and colonial capitalism that enabled them to be rich enough to be first in the vaccine queue and help themselves to large numbers of scarce doses. For example, Canada, a leading global exporter of moral puffery, had already snatched up about 80% more vaccines than it actually needed —  more than ten doses for every person in the country. It had even elbowed in on COVAX, an international program to ensure equitable global access to vaccines. Having looked after itself at the expense of others, Canada exuded generosity at  the G7 by promising to donate its “surplus” and to  fund other vaccine purchases.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.179
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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 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

Citations0
Published2021
Admission routes2
Has abstractyes

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