The Role of Ethnic Diversity in Stimulating Innovation Processes: Comparative Analysis of Poland, the Czech Republic and Hungary
Bibliographic record
Abstract
Purpose: Since existing literature suggests that ethnic diversity is one of the key elements that shape the dynamics of innovation, we examine whether inventions generated by ethnically diverse teams in the Czech Republic, Poland and Hungary are more valuable than those created by homogenous teams of native researchers. Design/Methodology/Approach: Using the OLS method, we estimate the parameters of the regression model in order to examine the relationship between ethnic diversity and the quality of technical solutions created as well as to determine which ethnic group and which combination of these groups (for each country) has the greatest impact on the quality of inventions. We take the frequency of citation as a measure of the quality of inventions, and the degree of ethnic diversity in the inventor team is measured using the Herfindahl index. Findings: Based on a cross-sectional data set being a sample of 2518 international patent applications (PCT) from 2004-2012, we have observed that both the mere presence of foreigners as well as greater ethnic diversity in the inventor team significantly increase the quality of technical solutions in Poland and Hungary, and moderately in the Czech Republic. Our study has also revealed that of all ethnic groups, Americans have the greatest impact on the citation of inventions, and it is the case in all three countries covered by the study. The optimal combination of individual groups, however, is different for each of these three countries: in Poland, the highest quality of inventions is related to the presence of citizens of the US, Belgium, Japan and Turkey, in Hungary – the US and Israel, and in the Czech Republic – the US, Germany and Canada. Practical Implications: The research results can be used by decision makers in Poland, the Czech Republic and Hungary when shaping the countries’ migration and innovation policies. Originality/Value: Original research.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".