Geographies of Capital Accumulation: Tracing the Emergence of Multi-polarity, 1980–2014
Bibliographic record
Abstract
Abstract To properly assess the relative places of China and the United States in the world system, the fact of the transformation of old, and the emergence of new, centers of capital accumulation needs to be established, and some attempt made to develop means of measuring these developments. This paper, working within the framework of Uneven and Combined Development, will suggest a new metric by which we can assess the geography of capital accumulation in the world economy, a metric with three components. The first component examines national income, both per capita and as shares of the world total. The second component refines the latter to an examination of share of world manufacturing, with a specific examination of distribution of the key sector of high-technology manufacturing. The third and final component examines the distribution of large corporations through the world economy, and introduces a new term – the relative weight of large corporations. All components of this metric suggest that key aspects of the modern economy remain “territorially bound” and clearly reveal the steady, long-term decline of the United States as the dominant center of capital accumulation, and the simultaneous emergence of new centers of capital accumulation in an increasingly multi-polar 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".