Abstract Relations between Estonia and
Bibliographic record
Abstract
Finland have been very strong traditionally. Their geographical proximity has facilitated the exchange of goods as well as ideas. Finland has a major stake in the Estonian economy – Finland is Estonia’s number one trade partner. Finnish organizations make up about a quarter of all foreign direct investment in Estonia. The two countries also share very strong cultural ties. Both peoples are of Finno-Ugrian origin, they speak similar languages. Many Estonians speak the Finnish language fluently. Although Estonia is a Baltic state from a geographical point of view, Estonians consider themselves more Nordic than Baltic. Yet, despite these similarities, Finnish companies operating in Estonia face major communication challenges with their Estonian business units. Half a century of Soviet occupation has left its mark. On the one hand it had introduced the lasting legacy of Soviet management style. On the other hand, it has led to widespread prejudice against Estonian businesses, which even fifteen years of restructuring and the adoption of contemporary management practices could not change. Cooperation between Finnish organizations and their Estonian counterparts is cumbersome due to prejudices, taken-for-granted assumptions and miscommunication. This paper analyses the communication problems by examining intra-organizational communication between Finnish parent companies and their Estonian subsidiaries. The findings of this paper are based on a survey conducted with Estonian and Finnish managerial and non-managerial staff. The paper will underline the importance of cultural sensitivity in business communication. Key words: Cross-cultural communication, Estonia,
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".