Uniformisation des taux de profit et hypothèse sectorielle
Bibliographic record
Abstract
The hypothesis of an equalization of sectorial rates of profit has an important role to play in the economic theory. Though it has been formulated a long time ago (A. Smith, D. Ricardo…), now as a definite principle (with its mechanism) and now as a postulate, it is however contradicted by statistical observation. There have been various answers to this problem. We propose to study it in a new light, emphasizing that the formulation of this "law" as well as its empirical evidence are registered within the traditional framework of activity sector. Then, we have to think about the latter as it has a fundamental role to play. But the activity sector is no longer a homogeneous category, for firms can considerably differentiate the conditions (technological and financial ones) of capital allowance, independently of any reference to the notion of product. Then, we must think over this problem, no longer wondering where this allowance of the capital is produced, but how. This brings us to propose the elaboration of a new classification of the firms, that leads to an analysis at an "intermediary level" other than the insufficient level that results from the division of the economy into activity sectors.
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 | 0.027 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".