Les déterminants des investissements directs européens dans les pays d’Europe Centrale et Orientale
Bibliographic record
Abstract
Cet article s’intéresse aux déterminants des investissements directs étrangers européens dans les pays d’Europe centrale et orientale. L’estimation est effectuée en données de panel et porte sur la période 1993-1998. Nous montrons que les écarts de coûts salariaux entre les PECO ne constituent pas un facteur déterminant des investissements étrangers contrairement au différentiel de salaire entre les pays de l’Union européenne (UE) et les pays candidats. L’effet du capital humain apparaît sensible à la construction de la variable d’éducation et au choix de l’échantillon tandis que l’importance de la taille des marchés, de la proximité géographique et du choix de méthode de privatisation contribuent à expliquer les flux d’investissements directs européens à destination des PECO.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".