Le contrôle gouvernemental des entreprises multinationales : Le cas des États-Unis
Bibliographic record
Abstract
The article is a case study of the relationship between the American government and US multinational corporation. It argues that while the state - MNE relationships vary from country to country, the US pattern is one of a very limited transnational role for government. Main factors in this pattern are the division of powers between the various branches and agencies of the US government, and changes in administrative staff following each national election. Few cases of government effort at business guidance are found: antitrust policy, foreign aid to friend governments, ineffectual protests again nationalisation of foreign subsidiaries of US MNE, exceptional cases of purposeful intervention, and the US adherence to international guide lines to MNE conduct sponsored by OECD. The article studies in more detail the case of oil, in which the US government is supposed to have intervened in a more direct way. The article concludes that US foreign policy is too complex to be understood simply in terms of government support of US multinational abroad. Besides us industry and the American government are themselves too split to produce a single and homogeneous pattern of policy.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".