Przedsiębiorczość akademicka. Dobre praktyki [Academic Entrepreneurship: Good Practices]
Bibliographic record
Abstract
Polish Abstract: Niniejsza publikacja jest zbiorem „dobrych praktyk przedsiebiorczości akademickiej zidentyfi kowanych nie tylko w Polsce ale takze w Stanach Zjednoczonych, Niemczech, Chinach, Szwajcarii, Belgii, Wloszech, Szwecji, Kanadadzie i Wielkiej Brytanii. Praktyki pochodzą z roznych sektorow gospodarki takich jak: biotechnologia, medycyna, farmacja, biofarmacja, elektronika i automatyka, polprzewodniki, nanotechnologia, elektronika, elektromechanika, analiza środowiskowa, informatyka, internet, multimedia i komunikacja. Wskazano takze przyklady „przedsiebiorczych uniwersytetow, w przypadku ktorych mamy do czynienia z świadomym nastawieniem calych spoleczności akademickich na rozwoj przedsiebiorczości w wielu sektorach gospodarczych jako podstawy wlasnej strategii rozwoju. Choc zaprezentowane przyklady mogą sie wydawac odlegle, to lączy je umiejetnośc przekuwania wiedzy generowanej w sferze nauki na sukcesy ekonomiczne. Korzyści osiągają nie tylko bezpośredni przedsiebiorcy akademiccy, ale takze innowacyjne przedsiebiorstwa, regiony czy kraje oraz same uczelnie. English Abstract: This publication is a collection of good of academic entrepreneurship identified not only in Poland, but also in the United States, Germany, China, Switzerland, Belgium, Italy, Sweden, Canada and the UK .. The practices come from various sectors of the economy such as: biotechnology, medicine, pharmacy, biopharmaceutics, electronics and automation, semiconductors, nanotechnology, electronics, electromechanics, environmental analysis, IT, internet, multimedia and communication. Examples of enterprising are also indicated, where we are dealing with the conscious attitude of entire academic communities at development of entrepreneurship in many economic sectors as the basis of own development strategy. Although the presented examples may seem distant, they are connected by the ability to translate knowledge generated in the sphere of science into economic successes. Not only direct academia benefits, but also innovative enterprises, regions or countries and universities themselves.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".