Etableringsproblem på den ryska marknaden : Svenska företag i Moskva och Sankt Petersburg
Bibliographic record
Abstract
In this essay we map and evaluate obstacles and problems that can occur during the establishment of international companies in Russia. This is done by investigating Swedish companies, which are established in Moscow and Saint Petersburg. We used secondary and primary data from books, previous surveys, reports, articles and the Internet. We also interviewed the following companies: Alfa Laval, Kockum Sonics AB, Höganäs Keramik, Skanska, Assa Abloy, Advakom, AnoxKaldnes, Lindab, Delovoj Peterburg, HL-Display and also a journalist from the Swedish Radio. Most of the problems named by the interviewees were similar, but some differences were also found. The differences were primarily found in the ranking of importance between the different problems. As a conclusion we can say that the most important factors were: · crime such as bribery · administrative problems such as licensing · tax laws and political system · culture and language. These problems can however be avoided to some point by hiring Russian consultants to manage the contacts and agreement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".