Review of Capitalism, The American Empire, and Neoliberal Globalization by Kenneth E. Bauzon
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".