Une « boîte noire » à manipuler soigneusement : L’analyse des coûts et des bénéfices sociaux de l’entreprise multinationale
Bibliographic record
Abstract
This paper assumes that the analysis of the social costs and benefits of the MNE is not only possible but also necessary. This kind of analysis is not easy because there are different rival theoretical models, because of the oligopoliste market structures that characterize multinational industries, and because the often clashing interest of home and host countries. However the author is able to identify the main costs and benefits for each group of countries. The home countries are often able to export products and factors, and to import steady flows of cheap raw materials and intermediate goods thanks to MNE. However they risk the loss of taxes, jobs and capital; besides they can suffer from MNE speculation against their own currency and balance of payments imbalances. Host countries show positive gains in terms of economic development, and eventual access to foreign markets. However they can loose taxes, suffer the extra-territorial implementation of home countries' laws and policies as well as transfer-pricing and balance-of-payments desequilibra.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".