Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it