Les aspects méthodologiques des mesures de la multi-nationalité : une analyse des firmes multinationales américaines1
Bibliographic record
Abstract
Cet article présente une discussion critique des mesures d’envergure et d’échelle utilisées pour évaluer la multi-nationalité. Il existe deux manières de mesurer la multi-nationalité (parfois appelée la diversification internationale) des entreprises. Une revue de la littérature portant sur l’utilisation des deux types de mesures est proposée et ensuite les deux mesures sont appliquées pour tester la nature régionale des activités internationales. Les résultats montrent que la méthode appropriée consiste à utiliser les mesures d’échelle qui captent le degré de multi-nationalité, comme la part des ventes réalisées à l’étranger. Cet article apporte une vérification empirique que les mesures d’envergure, qui comptabilisent le nombre de pays où l’entreprise a une filiale, ne sont pas satisfaisantes. L’article fournit une nouvelle mesure d’échelle des activités intra-régionales des grandes entreprises américaines.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".