Austria's New Statistics on Foreign Affiliates
Bibliographic record
Abstract
In 2007, 19% of all persons employed in Austria's market economy - some half a million people - worked in enterprises majority-owned by non-resident units. While accounting for just 3% of all domestic enterprises classified under sections C to K of the Austrian Statistical Classification of Economic Activities (ÖNACE) 2003, the foreign-controlled enterprises produced roughly one-third of the turnover generated and one-quarter of the gross value added by all enterprises in those sections. Foreign-controlled enterprises, moreover, accounted for more than 50% of corporate research expenditure. At the same time, enterprises resident in Austria controlled nearly 4, 300 enterprises abroad employing roughly 760, 000 persons. These foreign affiliates were located in a total of 81 countries throughout the world. The lion's share, though, was sited in Germany, followed by countries in Central, Eastern and Southeastern Europe. These figures and ratios are some of the key results of Austria's new statistics on foreign affiliates (Austrian FATS statistics) for the first reporting year (2007). For more details see the following report, which was co-authored by staff experts from the Oesterreichische Nationalbank and STATISTICS AUSTRIA and which has been published (in German) both in Statistische Nachrichten (STATISTICS AUSTRIA) and in Statistiken - Daten und Analysen (OeNB).
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.013 |
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".