Le capitalisme fondé sur la connaissance et le changement dans les stratégies des entreprises industrielles
Bibliographic record
Abstract
This paper explores the changing nature of contemporary capital accumulation focusing in particular on the increasing importance of knowledge inputs in the production process. The growing knowledge-intensity of production reflected in the role of design, research and development, marketing, management and advertising in the growth strategy of the firm, has had numerous consequences for the nature of competition amongst firms and for the internationalization of production. As increased knowledge-intensity of production gave rise to ever more rapid technological change in industry, the need for greater flexibility in production and labour processes became acute, more so as the global economic crisis deepened and competition from newly industrializing countries rose. Automation and sub-contracting were important new strategies. So too was the segmentation and delocalization of production processes to cheap labour countries in the Third World and Eastern Europe. More recently, as the costs and risks involved in R&D escalated, large corporations have also begun to decentralize knowledge production itself by funding research and development activities outside the MNC, and by internationalizing knowledge production itself through the establishment of research laboratories abroad or the implementation of a System of world product mandates for selected manufacturing subsidiaries.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".