Bibliographic record
Abstract
This bachelor work deals with the life and work of a Czech composer Milan Kymlicka. The aim of this work is to develop a closer view on his life especially in Czech Republic and in Canada, where he had emigrate because of the political situation. This thesis is divided into several chapters; the main theme is Milan Kymlička's biography, interviews and then finally a smaller sample of his work. The biography of Milan Kymlicka is based on telephone communications and materials, which Kymličkas wife Marie sent to me. Now she lives in Canada. It is about Kymličkas life just before he immigrated to Canada, then about living in Canada and in the end about his returning back to his homeland. The second part of this thesis deals with the interviews - the most important part. Those interviews were led with friends and colleagues of Milan Kymlicka, who provided me a telephone, or a personal interview about their common experience and work experience. The interviews are with Vit Král, Varhan Orchestrovič Bauer, Joseph Pokluda, Juraj Ďurovič. This section also contains an article written by Christopher Dedrick, the orchestrator of Milan Kymlicka, who describes Kymlička in all aspects. The third part is analysing one of the Kymlička's score and approaching the plot and contend of animated series Rupert The Bear....
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".