Bibliographic record
Abstract
Shrnutí v anglickém jazyce / Resumé in English The purpose of my thesis is to analyse one of the most controversial topic, which people often discuss, the death penalty. To bring an option for a potential reader how to to make his own attitude to the death penalty was the collateral aim. In recent decades the most states abolished death penalty. But there is still over one quarter states in the world, which death penalty aply. Neverthelles public opinion polls show, that public support is relatively significant. The thesis is composed of nine chapters. Chapter one is introductory and defines the purpose of this thesis. Chapter two deals with the punishment and its purpose. This chapter consists of two parts. Part one concentrate on the punishment and defines, what this concept means. Second part concentrate on the purpose of the punishment, on the absolute and relative theory, on the purpose of the death penalty and on the purpose of the punishment in the czech penal code. Chapter three describes the history of the death penalty. The chapter consists of three parts. Part one deals with general history of the death penalty. Part two concentrate on the history of the death penalty in our area. Part three describes the most frequent method of the death pealty. Chapter four concentrates on the arguments of...
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.060 | 0.025 |
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".