News / Information
Bibliographic record
Abstract
The two-day conference "Hidden Champions in CEE and Dynamically Changing Environments", presenting the outcomes of the first research on "Hidden Champions" in Central and Eastern Europe (CEE), Turkey and Kazakhstan -highly innovative, differentiated and specialized small to medium size companies holding lead market positions in their field internationallysuccessfully concluded.The international event organized by CEEMAN international management development association, in cooperation with IEDC-Bled School of Management and Austrian Federal Chamber of Commerce (WKO), revealed 165 successful companies from the countries of CEE, Turkey and Kazakhstan, their business success trajectories and distinctive business and leadership practices.Over 130 business leaders, business thinkers, investors, deans of business schools, researchers, policy makers and media from 31 countries spoke about Hidden Champions as core pillars of open economies.View conference presentations here.The concept of Hidden Champions was initially identified and studied by Prof. Hermann Simon, world recognized expert in strategy, marketing and pricing, referred to as the most influential management thinker after the late Peter Drucker in German speaking area.According to the 1996 study performed on the German economy and the 2009 study extended from Germany also to Austria and Switzerland, Prof. Simon re-confirmed that Hidden Champion-type of companies present an important pillar of advanced economies of Germany, Austria and Switzerland.The purpose of CEEMAN-IEDC 2010/11 research project, conducted in 18 countries by a group of over 50 researchers, from management education institutions under CEEMAN leadership and in cooperation with RABE-Russian Association of Business Education and Polish Association of Management
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.527 | 0.432 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".