The Impact of the Foreign-Speaking Population of Estonia on the Development of Highly Innovative Services
Bibliographic record
Abstract
Aim and tasks. To determine the influence of non-Estonian-speaking population on the development of network structures in the highly innovative services sector of Estonia. To achieve the objective of the study, the following objectives were set: analysis of literary sources on the topic of the study; determine the role of the non-Estonian-speaking population in the sector of highly innovative services of Estonia; conduct a correlation and regression analysis of indicators characterizing the influence of the non-Estonian-speaking population of Estonia on network structures in the sector of highly innovative services. Methods. To determine network structures, an indicator from the collection “Science. Technology.Innovation” of the Estonian statistical service was used, which characterizes the number of enterprises that had partners in the field of innovation activities. Data on cooperation between Estonian organizations is published for a two-year period. In the correlation and regression analysis, the data referred to the last year in the period. As a characteristic of the non-Estonian-speaking population, indicators of the population employed in the economy were used. Results. The role of the non-Estonian-speaking population in the Estonian economy, including in the Estonian highly innovative services sector, was analyzed. During the period under review, a third of the employed population of Estonia with higher education are non-Estonian-speaking. A quarter of all employed specialists in the top and middle management levels are also non-Estonian-speaking. In the sectors “Information and Communication” and “Professional, Scientific and Technical Activities”, the non-Estonian-speaking population makes up 1/5 of all employed. Based on the results of the regression analysis, a statistically significant and reliable regression model was identified, indicating that the development of network structures in the information and computer services sector is influenced by Russian-speaking specialists at the “Manager” level. Conclusions. Despite the aggressive Estonianisation policy pursued by the Estonian leadership, the non-Estonian speaking population, especially the Russian speaking population, plays an important role in key sectors of the Estonian economy. The issue of linguistic security and diversity is an important element of the nation’s self-identification, so ill-considered decisions can lead to increased social tension. At the same time, limiting the rights of a third of the population can not only aggravate social tension in society, but also have a negative impact on the economic development of the entire country.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".