Bibliographic record
Abstract
The 1990s, especially in the United States, witnessed an unprecedented change in income distribution, with a large redistribution towards rentiers on the one hand, and towards the upper ranks of the managerial bureaucracy on the other hand, as became ever more obvious after the financial scandals affecting large corporations such as Enron and Worldcom. This has also been accompanied by large capital gains that benefited top-file managers as well as shareholders. Ordinary employees and workers, as a counterpart, have seen their real purchasing power stagnate. Despite all this, and in contrast to the predictions of the canonical Kaleckian growth model, many countries achieved respectable growth rates of capital and output. The purpose of the present paper is to explain this paradox and to provide a consistent post-Keynesian model of growth that would model the main features identified above, making a distinction between managerial labour, basically overhead labour, and workers, essentially direct labour – a distinction that was recommended, but never implemented by Kaldor. The model is based on target-return pricing procedures. We then study the implications of cadrisme, a managerial-friendly regime based on large pay packages for the managerial class.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".