Low surplus value historically required for accumulation, seen in a model derived from Marx
Bibliographic record
Abstract
In Volume I of Capital, Marx offers actual data from a Manchester spinning factory describing that business. In Volume II, he offers schemes of reproduction to help understand accumulation of capital while mentioning numbers that actually suggest correlation to the spinning factory data. Nevertheless, Marx seems to slide over the costs of new machinery when analyzing accumulation, instead focusing on wear and tear (depreciation). In this chapter, we offer a modeling of accumulation that takes account of modern estimates of the composition of capital, that is, the relation of labor time invested in constant capital compared to the labor time employed with that constant capital, relying principally upon U.S. and Canadian estimates. We find empirically that the composition of capital fluctuates but does not show much trend. We also consider levels of the rate of exploitation and of utilization of surplus value required for achieving actual historical levels of accumulation of capital, and include consideration of the turnover of capital. We find that only a small portion of surplus value, perhaps 10%, is required for actually achieved accumulation. This suggests that a focus on the utilization of surplus value for the accumulation of capital misses vast other terrains for the utilization of surplus value. Our result is suggestive of an overemphasis within Marxist political economy on accumulation of capital.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".