Gods outside the Market: Central Banks, China and the Emergence of Neoliberal State Capitalism
Bibliographic record
Abstract
Capitalism has always had to make use of contrivances from outside the realm of the market to compensate for structural problems of valorization. These “gods outside the market” have included the forced labour and slavery characteristic of the mercantilist era, as well as the scramble for colonies in the period leading up to World War I. With the anti-colonial movement and the closing off of external avenues to assist valorization, there have been moves to discover new “gods outside the market,” but internal rather than external to national states. The nationalizations of Fannie Mae and Freddie Mac are relatively “familiar” forms of such state intervention. Less familiar, but more important, has been the newly important role of central banks, using the expansion of their asset base (most familiarly but not exclusively through Quantitative Easing) to supply the liquidity which the market cannot. Together, these developments suggest two things: first, that neoliberalism in saving itself from the effects of the Great Recession has simultaneously transformed itself. I am suggesting the term “neoliberal state capitalism,” and second, that central to this process have been actions centred in China, actions too often seen as derivative and not constitutive of developments in the world economy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".